thesis model laser cutting

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Issue		2, 2015


Article Number		20
Number of page(s)		15
DOI
Published online		26 October 2015

1. Introduction and background

2. taguchi methodology, 3. experimentation, 4. design of experiments and taguchi method, 5. response surface methodology (rsm), 6. results and analysis, 7. rsm for the kerf taper ( t a ) and the average surface roughness ( r a ), 8. validation of models, 9. verification experiments, 10. critique of methodology, 11. conclusions, acknowledgments.

List of tables
List of figures

Research Article

Modeling and optimization of laser cutting operations

Mohamed Hassan Gadallah 1 * and Hany Mohamed Abdu 2

1 Mechanical Design & Production Engineering Department, Faculty of Engineering, Cairo University Egypt, 12316 Cairo, Egypt 2 Production Engineering & Design Department, Faculty of Engineering, Minia University, 61516 Minya, Egypt

* e-mail: [email protected]

Received: 4 June 2015 Accepted: 22 August 2015

Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L) considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper ( T a ), surface roughness ( R a ) and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L 27 OA) are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs) are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA) and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.

Key words: Optimization / Laser cutting / Kerf width / Taguchi technique / Response surface methodology / Design of experiments

Nomenclature

LBM: Laser beam machining

RSM: Response surface methodology

DOE: Design of experiments

T a : Kerf taper

R a : Average surface roughness

Nd:YAG: Neodymium:yttrium-aluminum-garnet

S/N: Signal to noise ratio

OA: Orthogonal array

L 27 OA: Orthogonal array of 27 experiments

ANOVA: Analysis of variance

ANOM: Analysis of means

X 1 : Power

X 2 : Assist gas pressure

X 3 : Pulse frequency

X 4 : Cutting Speed

Laser Beam Cutting (LBC) is an important nontraditional cutting process. It is used to shape engineering materials with complex shapes and strict design and performance functional requirements. The process is used for cutting, drilling, marking, welding, sintering and heat treatment processes [ 1 ]. Applications of laser sheet cutting include aerospace, automobile, shipbuilding, electronic and nuclear industries. The intense laser light is capable to melt almost all materials [ 2 ]. Laser cutting is a thermal energy based non-contact process, therefore does not require special fixtures and jigs to hold the work piece. In addition, it does not need expensive or replaceable tools to produce mechanical force that can damage thin, intricate and delicate work pieces [ 3 ]. The effectiveness of laser cutting depends on the thermal, optical and mechanical properties of materials. Therefore, materials with high degree of brittleness, hardness and favorable thermal properties (low thermal diffusivity and conductivity) are suitable for laser cutting operations [ 4 ]. High speed steels, ceramics, composites, diamonds, plastics and rubber are typical candidate materials.

Nd:YAG (Neodymium:yttrium-aluminum-garnet) and CO 2 are the most widely used laser applications [ 9 ]. Nd:YAG laser is an optically pumped solid state laser, working at a wavelength of 1.06 μm. CO 2 laser is an electrically pumped gas laser that radiates at wavelength of 10.6 μm [ 2 , 4 ]. CO 2 laser is used in fine cutting of sheet metals at high speeds as it has high average beam power, better efficiency and good beam quality. Nd:YAG laser has low beam power operating in pulsed mode. High peak power is capable to cut thicker materials for different applications [ 5 ]. Due to shorter wavelength of Nd:YAG laser, it is reflected to a lesser extent by metallic surfaces and high absorptivity of Nd:YAG laser cutting highly reflective materials with relatively less power [ 6 ]. Therefore, Nd:YAG laser is suitable for processing of metals in general and reflective materials in particular. Gases employed include oxygen, nitrogen and argon. A similar study is carried on Ni base super alloys [ 7 ].

Austenitic stainless steel (316L) is an anti-corrosive and anti-staining materials [ 8 ]. The alloy form of stainless steels is milled into coils, sheets, plates, bars, wire, and tubes. Typical applications include food preparation equipments (particularly in chloride environments), pharmaceuticals, marine, architectural, medical implants (orthopaedic implants like total hip and knee replacements) and fasteners. Grade 316 is the standard molybdenum-bearing grade, secondary to 304 amongst the austenitic stainless steels. The molybdenum gives 316 better overall corrosion resistant properties than Grade 304, particularly pitting and crevice corrosion in chloride environments. Grade 316 (with low carbon is immune from sensitization due to grain boundary carbide precipitation). Thus, it is extensively used in heavy gauge welded components (≥6 mm). There is no significant price difference between 316 and 316L stainless steel. The austenitic structure gives these grades excellent toughness, even down to cryogenic temperatures. Compared to chromium-nickel austenitic stainless steels, 316L stainless steel offers higher creep, stress to rupture and tensile strength at elevated temperatures. Some authors studied CO 2 laser cutting on Kevlar 49 composite materials [ 19 ]. Kerf width, dross height and slope of cut are typical process responses. Table 1 gives the chemical composition of 316L stainless steel employed.

Chemical composition of stainless steel (316L) (wt.%).

Input process parameters and levels used in the designed experiments.

The quality of cut depends upon the combination of process parameters such as laser power, type and pressure, cutting speed, sheet thickness, frequency and chemical composition. Researchers have investigated the effect of laser cutting parameters on cut geometry and cut surface quality. They applied one-factor at a time approach to study the effect of process parameters on responses. This approach consumes time and effort for large number of experimental runs because only one factor is varied, keeping all other factors fixed. The interaction effects among various process parameters are not considered which may be of interest in some studies; not to mention higher level interactions.

Li et al. [ 12 ] applied Taguchi robust design methodology to study the depth of cut, width of cut and Heat Affected Zone (HAZ) during laser cutting of Quad Flat No-lead (QFN) packages using a Diode Pumped Solid State Laser (DPSSL) system. Three control factors such as laser frequency, cutting speed, and laser driving current contributed greatly to laser cut quality. Tosun and Ozler [ 13 ] applied Taguchi methodology for optimization of surface roughness and tool life simultaneously during hot turning of high manganese steel work piece using the sintered carbide tool. The effect of hot turning parameters (cutting speed, depth of cut, feed rate and work piece temperature) on multiple performance characteristics is discussed.

Huehnlein et al. [ 23 ] employed design of experiments on the cutting of Al 2 O 3 ceramic layers. One factor at a time and interaction effects of decision variables are very time consuming. The burr at the kerf is employed as a response for elimination. Process parameters include laser power, cutting speed, distance from nozzle to surface, assist gas pressure, position to the focus and diameter of the nozzle. Velocity and gas pressure prove significant parameters. Forty six experiments are used to carry response surface modeling.

Sharma and Yadava [ 18 ] used laser beam cutting for precise cutting of Al alloy sheet metals. Four process parameters are used to optimize kerf quality (kerf width and kerf deviations) characteristics; these are gas pressure, pulse width, pulse frequency and cutting speed. Standard orthogonal arrays are used for experimentation. An L 9 OA is employed to host the variations of 4-3 level factors. This means that 2 factors are confounded. Interaction effects can be read in columns 3 and 4 respectively because of degree of freedom requirements [ 15 ]. Similar work is reported for Al-Alloy sheets [ 10 ].

Brecher et al. [ 2 ] developed a novel process concept for Laser Assisted Milling (LAM) with local laser induced material plastification before cutting. Results are presented for Nickle based alloy Inconel 718 using TiAlN coated cemented carbide cutting tool.

Adelmann and Hellmann [ 24 ] described a fast algorithm to optimize the laser parameters for laser fusion cutting process. The objective is to obtain a burr free laser cut. The algorithm performs on a one at a time design of experiments basis. Parameters include laser power, focal position and gas pressure. The algorithm is known as Fast Laser Cutting Optimization Algorithm (FALCOA). The study is limited to 1 mm Al sheets using a 500 W single mode fiber laser.

Miroslav and Milos [ 21 ] presented a complete review study on CO 2 laser cutting with respect to materials employed (alumina, slate, mils steel, stainless steel 37, polymers, composites, wood, high strength low alloy steel, aluminum copper, titanium, Kevlar, plastic, rubber, and aluminum composite), input process parameters (laser power, cutting speed, nozzle distance, gas pressure, gas type, focus position, laser cutting mode, laser pulse frequency, work piece thickness, duty cycle) and process responses (kerf taper and width, surface roughness, heat affected zone, striation formation and dross formation). As a new process with nontraditional nature, the objective is to design the laser cutting process for minimum outputs such as kerf width and taper, minimum surface roughness and minimum heat affected zone.

Rajpuohit and Patel [ 16 ] studied Laser cutting quality characteristics. Periodical lines (striations) are considered as noise affecting surface roughness and geometry precision of laser cut product. The mechanism leading to striations is not fully understood.

Phipon and Pradhan [ 20 ] used Genetic Algorithms to optimize laser beam machining operations. Minimum kerf taper and surface roughness are taken as process responses. Response surface methods are used to develop mathematical models relating responses to process parameters. Good prediction capabilities are obtained from this study. A Central Composite Design (CCD) of 31 points and 5 levels is employed for experimentation. This is a highly fractional array compared with 5 4 = 625 experiments required by full factorial design. Chaki et al. [ 17 ] integrated a model of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction and optimization of quality characteristics of Al alloy during pulsed Nd:YAG laser cutting. The ANN serves the purpose of modeling and prediction of surface roughness and material removal rates. Other outputs can be added at any stage. The ANN model allows prediction within and outside process parameter ranges compared with any mathematical modeling techniques that allow prediction within parameter ranges. This study represents a good reference in relation to process single and multi objective optimization, modeling using ANN, past studies of the subject using Taguchi method, response surface methodology, and grey relational analysis.

Genichi Taguchi developed a three stage methodology back in the 80s [ 14 , 15 ]. The three stages are: systems design, parameter design and tolerance design. Figure 1 shows a procedure of Taguchi method [ 14 , 15 ]. In the present work, four control factors with three levels of each are considered. An L 27 OA is employed to plan experimentation due to reasonable number of experiments and interaction effects among variables. This means a total of 3 4 = 81 experiments for a full factorial design is needed or 27 experiments for a fractional factorial design.

Procedure of Taguchi method [ ].

define the control variables and their practical domain in reality and in specific to the machine employed,

define the # of levels, each control variable can have,

define an appropriate orthogonal array host this experiment.

Experimental design using L 27 OA.

A proper understanding of the limitations of these arrays is needed. Three replications at each setting of control variables are obtained. The three replications are used to obtain the mean, standard deviation and signal to noise ratio of response respectively.

The experiments are conducted on a 200 W pulsed Nd:YAG laser beam machining system with CNC work table (ROFIN DY x55-022 model) as shown in Figure 2 . As an assist gas, oxygen is used and passed through a conical nozzle of 1.0 mm diameter co-axially with laser beam. The laser beam is focused using a lens with focal length of 50 mm, and the minimum diameter of focused beam is about 0.47 mm, stainless steel (316L) sheet with 3 mm thickness. Nozzle diameter, focal length of lens 200 mm, nozzle standoff distance and sheet material thickness are kept constant throughout experimentation.

Laser cutting machine utilized in this study.

Schematic of laser cut kerf [ ].

R a value is measured using the Surface Roughness Tester (TAYLOR-HOBSON – SURTRONIC 3, 112/1500 – 1150483, DENMARK). All measurements are acquired using 4.00 mm evaluation length. Average values of T a and R a corresponding to each setting are also given in Appendix .

In this study, the Taguchi parameter design method is used to determine optimal machining parameters for minimization of T a and R a . Four control factors: X 1 , X 2 , X 3 and X 4 and three interactions: X 1 · X 2 , X 1 · X 3 and X 1 · X 4 are considered. The experimental observations are further transformed into lower the better signal-to-noise (S/N) ratio for the kerf taper and surface roughness [ 15 ].

Where y i are the observed data (or quality characteristics) of the i th trial and n is the number of replications. Similar work is cited by El-Taweel et al. on Kevlar 49 composite materials using CO 2 Laser [ 19 ].

Where X is the matrix of factors level and Y is the force responses. A certain domain may be in need for several RSM model polynomials to model adequately. The evaluation and presence of curvatures are dealt with by using 3-level orthogonal arrays respectively. Analysis of variance is used to formally test for significance of main and interaction effects. A common approach consists of removing any non-significant term from the full model. Analysis of variance was performed initially to screen out non significant variables. Several decision rules are employed to judge whether a term should be included or excluded from the full model. Other attempts deal with multi-response problems using the desirability function. In our opinion, this is not an objective index and hence, the resulting optimum has to be interpreted with care. Multi-variate responses may have several difficulties resulting from dependencies among error estimations, error among expected value of responses and linear dependencies in the original data [ 25 ].

Adequacy of models is checked by several tools such as residual analysis, normal probability plots, model form modifications, etc. Several approximations are developed for the response surfaces and verified further by additional experiments.

Analysis of Variance (ANOVA) is a statistical technique for quantitative estimation of relative contribution of each control factor on overall measured response. The relative significance of factors is often represented in terms of F -ratio or percentage contribution [ 13 ]. The F -ratio indicates more significance of the factor. In the present work, ANOVA is employed for analyzing significance of X 1 , X 2 , X 3 and X 4 on combined kerf quality parameter and surface roughness given in Tables 4 and 5 . An estimate of the sum of squares for the pooled error can be obtained by pooling the sum of squares of factors with the lowest sum of squares of X 3 , X 4 and all relevant interactions. The pooled error has 16 degrees of freedom and a sum of squares of 53.516. Hence, the pooled mean square error is 3.3447. The F -value is the ratio of the mean square factor to the variance of pooled error. X 1 and X 2 are significant parameters affecting the kerf taper quality at 99% confidence level.

Analysis of Variance (ANOVA) for the kerf taper a .

Analysis of Variance (ANOVA) for the average surface roughness a .

On the other hand, an estimate of the sum of squares for the pooled error can be obtained by pooling the sum of squares of factors with the lowest sum of squares of X 3 , X 4 and all relevant interactions. The pooled error has 16 degrees of freedom and a sum of squares of 28.688. Hence, the pooled mean square error is 0.6013. X 1 and X 2 are significant parameters affecting the surface roughness at 99% confidence level.

The results of the ANOVA with the kerf taper and surface roughness are shown in Tables 4 and 5 , respectively. This analysis was carried out for a significance level of α = 0.01, i.e. for a confidence level of 99%. Tables 5 and 6 show the P -values, that is, the realized significance levels, associated with the F -tests for each source of variation. The sources with a P -value less than 0.01 are considered to have a statistically significant contribution to the performance measures.

Analysis of Variance (ANOVA) for the heat affected zone (HAZ).

Table 4 shows that the only significant factor for the power is X 1 , which explains 79.86% of the total variation. The next largest contribution comes from pressure with 11.61%, which does not have statistical significance. The frequency and cutting speed the interactions have much lower levels of contribution.

Multiple quality characteristic ( R a ) is shown in Table 5 shows that the only significant factor for the power is X 1 , which explains 84.53% of the total variation and the next largest contribution comes from pressure with 10.28%. This does not have statistical significance. The frequency and cutting speed the interactions have much lower levels of contribution. Similar results are given in Table 6 for the Heat Affected Zone (HAZ). The effect of different operating parameters on S/N ratio comprising the kerf taper is shown in Table 7 and Figure 4 . It is clear that, optimum levels of different control factors for obtaining minimum kerf taper is: cutting speed at level 1 (150 W), pressure at level 1 (0.5 MPa), pulse frequency at level 3 (125 Hz) and cutting speed at level 3 (40 cm/min).

Effect of laser cutting parameters on S/N ratios ( ).

Effect of factors on S/N ( T a ) a .

Optimum levels of different control factors for obtaining minimum kerf taper is: cutting speed at level 1 (150 W), pressure at level 1 (0.5 MPa), pulse frequency at level 3 (125 Hz) and cutting speed at level 3 (40 cm/min). Relative contribution of the controlling parameters on kerf quality is shown in Table 7 .

The effect of different operating parameters on S/N ratio comprising the surface roughness is shown in Figure 5 .

Summary of control factors effects (S/N ratio values) are gives in Appendix .

Comparison of experimental and predicted results for kerf taper.

The average percentage deviation in the kerf taper and surface roughness based on S/N ratio values are 21.14% and 2.86% respectively. Table 7 indicates that the average percentage accuracy in the kerf taper and surface roughness based on S/N ratio values are 78.86% and 97.14% respectively.

Figures 6 and 7 give the measured vs. predicted kerf taper based on S/N ratio and surface roughness.

Comparison of experimental and predicted results for surface roughness.

Response surface plots of kerf taper as function of different process variables are given in Figures 8 – 12 . Similarly, response surface plots of surface roughness are given in Figures 13 – 18 respectively.

Response surface plot of with power and oxygen pressure.

Response surface plot of with power and frequency.

Response surface plot of with power and cutting speed.

Response surface plot of with pressure and cutting speed.

Response surface plot of with frequency and cutting speed.

Response surface plot of with power and pulse frequency.

Response surface plot of with pressure and frequency.

Due to the pulsed nature of Nd:YAG laser cutting process, it is very difficult to obtain high surface quality. Therefore, the relative effects of laser cutting parameters such as power, oxygen pressure, pulse frequency, and cutting speed on R a during laser cutting of stainless steel (316L) is needed. The combined effects of power and oxygen pressure on R a are shown in Figure 13 . Pulse frequency and cutting speed are taken as constant values of 75 Hz and 20 cm/min, respectively. The surface plot reflects that power has linear effect on R a at different assisted oxygen pressure.

At high level of power, variation in R a value is large but at lower level of power, variation in R a is relatively less with respect to the oxygen pressure. Oxygen pressure and cutting speed are taken as constant at (1 MPa) and (20 cm/min) in Figure 14 .

Figures 15 and 16 show the effect of power, cutting speed and pressure, frequency respectively on R a keeping pressure, pulse frequency and power and cutting speed respectively as a constant value. It is also observed that power, pressure at low level the surface roughness is relatively less with respect to cutting speed and frequency respectively.

Figure 17 shows the effects of pressure and cutting speed on R a keeping the power and pulse frequency as constant (at middle value). It is observed that the nature of variation of R a with applied pressure for the different cutting speeds is same as shown earlier in Figure 18 with applied pulse frequency. Here, R a first decreases and then increases following a curved shape with the increase in pressure and pulse frequency. However, R a decreases with the decrease in cutting speed.

Table 8 gives the settings of the confirmation experiments for the laser cutting process. The five settings are taken at the lower and maximum limits of the power, oxygen pressure, frequency and cutting speed. Three replications are taken for the kerf taper (degree), average surface roughness (μm) and heat affected zone (mm). The mean, standard deviation and signal-to-noise ratios are calculated and compared later to prediction models.

Validation experiments and corresponding kerf taper, average surface roughness and heat affected zone.

Table 9 gives a comparison between the surface roughness measurements (μm) using Taguchi and RSM approaches .This comparison is gives in terms of the mean, standard deviation and signal-to-noise ratios. Using the mean as a measure, the models developed earlier deviate from actual measurements from −4.99% to +9.32%. Using the standard deviation as a measure, the models developed deviate from actual measurements from −146% to +769.8%. Using the S/N ratio as a measure, the models developed deviate from actual measurements from 1.12% to 14.776%. Accordingly, it is recommended to use the developed models to predict the average and signal to noise ratio of surface roughness.

Mean, S/N and standard deviation of surface roughness using Taguchi method vs. RSM.

Table 10 gives confirmation and prediction results for the kerf taper in degree. Using the mean as a measure, the developed earlier deviate from the actual measurements from −6.450% to +2.43%. Using the signal to noise ratios as a measure, the models deviate from the actual measurements from −105% to +149%. Using the standard deviations as a measure, the models deviate from the actual measurements from −649% to +12.79%. According, it is recommended to use developed models to predict mean kerf taper in degree.

Kerf taper using Taguchi method vs. RSM for the validation experiments.

Table 11 gives confirmation and prediction results for the heat affected zone. Using the mean as a measure, the different between the developed and predicted models vary from −4.35% to +8.24%. Using the standard deviation as a measure, the different between the developed and predicted models vary from −778% to +462%. Using the S/N ratio as a measure, the different between the developed and predicted models vary from −53.4% to 66.4%. Accordingly, it is recommended to use the developed models to predict the average HAZ.

Mean, S/N and standard deviation of the HAZ using the Taguchi method vs. RSM.

Several critiques can be mentioned for the experimental design chosen.

L 27 OA is used to host 4-3 level variables. This results in 81 experiments and L 27 OA is simple a 1/3 the number of experiments chosen. The 4-3 level variables result in six interaction effects; these are X 1 · X 2 , X 1 · X 3 , X 1 · X 4 , X 2 · X 3 , X 2 · X 4 , X 3 · X 4 . Only four interactions due to search graph limitation are considered.

The approach taken allows minimization of kerf taper, surface roughness and heat affected zones one at a time due to the usual limitations of design of experiments in dealing with several responses. There is a need for multi objective optimization formulation of laser cutting operations.

Other sources of noise for laser cutting operations need to be identified, modeled and optimized.

A modified model can be developed by adding L 27 OA and the 10 experiments. This will result in 37 experiments. The revised model will be more adequate model.

Results of Taguchi optimization indicates that best kerf quality are power at low level 150 W, gas pressure at 0.5 MPa, pulse frequency at high level 125 Hz and cutting speed at 40 cm/min. At the same average surface roughness are power at low level 150 W, gas pressure at 0.5 MPa, pulse frequency at low level 25 Hz and cutting speed at 20 cm/min.

Power and Assist gas pressure significantly affect the kerf quality in the operating range of process parameters.

Ta is found to be significantly affected by power, oxygen pressure, pulse frequency, cutting speed and interaction effect of oxygen pressure and frequency. On the other hand, R a is found to be significantly affected by power, oxygen pressure, pulse frequency, cutting speed, interaction effect of oxygen pressure and cutting speed.

Validation of RSM models indicates average percentage deviation in the kerf taper and surface roughness based on S/N ratio values are 21.14%, and 2.86% respectively.

From the response surface plot, it is observed that the pulse frequency and cutting speed have less effects on T a compared to other parameters. But lower value of R a can be obtained at lower level of process parameters except cutting speed in the present study.

Utilize search graph techniques to assign X 1 , X 2 , X 3 , and X 4 and respective interactions X 1 · X 2 , X 1 · X 3 , X 1 · X 4 , X 2 · X 3 , X 2 · X 4 , and X 3 · X 4 [ 15 ]. Interactions may become important if looked at thoroughly although others have ignored their effects [ 19 ].

Ten confirmation experiments are carried to verity models developed previously. The models developed show good prediction capabilities for the kerf width, surface roughness and heat affected zone as given in Table 12 .

Experimental vs. predicted results.

Special appreciation are due to CMRDI, Helwan, Egypt for allowing to carry all required experimentation and validation of models.

Experimental observations using L 27 OA.

Expt. no.	(deg.) (with three replications)			(μm) (with three replications)

1	0.35	0.33	0.28	4.00	2.33	3.50
2	0.19	0.30	0.22	3.40	4.50	3.80
3	0.33	0.22	0.25	3.00	3.60	3.40
4	0.27	0.22	0.18	4.90	3.50	3.50
5	0.41	0.34	0.27	3.33	4.60	4.50
6	0.32	0.31	0.27	3.75	4.60	4.66
7	0.22	0.19	0.23	4.63	4.17	4.75
8	0.51	0.32	0.41	4.50	4.99	5.20
9	0.36	0.31	0.29	5.75	5.00	5.50
10	0.38	0.42	0.57	5.03	5.92	5.87
11	0.39	0.45	0.38	5.65	5.86	6.33
12	0.45	0.41	0.59	5.50	6.88	5.57
13	0.67	0.45	0.55	4.30	6.50	5.33
14	0.54	0.65	0.46	5.94	6.52	6.37
15	0.66	0.57	0.41	5.37	6.53	6.55
16	0.94	0.75	0.88	6.40	6.83	6.00
17	0.86	0.66	0.77	6.31	6.68	6.30
18	0.88	0.78	0.67	6.60	6.50	6.98
19	0.65	0.59	0.87	6.87	6.89	7.50
20	0.73	0.88	0.62	7.22	6.94	7.22
21	0.87	0.71	0.66	7.44	6.89	7.16
22	0.95	0.89	0.88	7.01	7.81	7.30
23	0.89	0.87	0.77	7.75	8.20	9.83
24	0.74	0.68	0.98	8.87	9.20	9.58
25	1.23	1.75	1.51	8.96	8.85	9.40
26	1.20	1.55	1.30	9.10	9.40	9.19
27	1.33	1.45	1.60	9.85	9.87	9.40

Results of the confirmation experiment for S/N ratios values.

	Experiment	Prediction
The kerf taper
Optimal level	X , X	X , X
Kerf taper S/N ratio (dB)	−48.893	−47.944
Surface roughness
Optimal level	X	X
Surface roughness S/N ratio (dB)	−126.732	−133.565

Results of the confirmation experiment for mean values.

	Experiment	Prediction
Kerf taper
Optimal Level	X , X , X	X , X , X
The kerf taper mean values	1.70611	1.67397
Surface roughness
Optimal level	X , X	X , X
Surface roughness mean values	14.546	15.068

Results of the confirmation experiment for standard deviation values.

	Experiment	Prediction
Kerf taper
Optimal level	X , X	X , X
The kerf taper standard deviation	0.0125	0.0363
Surface roughness
Optimal level	X	X
Surface roughness standard deviation	−0.140	−0.114

A.K. Dubey, V. Yadava, Robust parameter design and multi-objective optimization of laser beam cutting for cutting for aluminium alloy sheet, Int. J. Adv. Manuf. Technol. 38 (2008) 268–277. [CrossRef] [Google Scholar]
C. Brecher, M. Emonts, C.-J. Rosen, J.-P. Hermani, Laser assisted milling of advanced materials, Phys. Procedia 12 (2011) 599–606. [CrossRef] [Google Scholar]
D. Schuocker, Laser cutting, Mater. Manuf. Process. 4 (2007), 311–330. [CrossRef] [Google Scholar]
K.A. Ghany, M. Newishy, Cutting of 1.2 mm thick austenitic stainless steel sheet using pulsed and CW Nd:YAG laser, J. Mater. Process. Technol. 168 (2005) 438–447. [Google Scholar]
G. Chryssolouris, Laser machining – theory and practice (mechanical engineering series), Springer-Verlag, New York, 1991. [Google Scholar]
A.K. Dubey, V. Yadava, Laser beam machining – a review, Int. J. Mach. Tools Manuf. 48 (2008) 609–628. [Google Scholar]
G. Thawari, J.K. Sarin Sundar, G. Sundararajan, S.V. Joshi, Influence of process parameters during pulsed Nd:YAG laser cutting of nickel-base superalloys, J. Mater. Process. Technol. 170 (2005) 222–239. [CrossRef] [Google Scholar]
http://www.aisi-stainless.com/Selling-list/316L-Stainless-Steel-supplier-316L-stainless-steel-Applications.html [Google Scholar]
A. Sharma, V. Yadava, R. Rao, Optimization of kerf quality characteristics during Nd:YAG laser cutting of nickel based superalloy sheet for straight and curved cut profiles, Opt. Lasers Eng. 48 (2010) 915–925. [CrossRef] [Google Scholar]
M. Boutinguiza, J. Pou, F. Lusquinos, F. Quintero, R. Soto, M. Perez-Amor, K. Watkins, W.M. Steen, CO 2 laser aluminum alloy sheet, Int. J. Adv. Manuf. Technol. 38 (2008) 268–277. [CrossRef] [Google Scholar]
C. Karatas, O. Keles, I. Uslan, Y. Usta, Laser cutting of steel sheets: influence of workpiece thickness and beam waist position on kerf size and stria formation, J. Mater. Process. Technol. 172 (2006) 22–29. [CrossRef] [Google Scholar]
C.H. Li, M.J. Tsai, C.D. Yang, Study of optimal laser parameters for cutting QFN packages by Taguchi’s matrix method, Opt. Laser Technol. 39 (2007) 786–795. [CrossRef] [Google Scholar]
N. Tosun, L. Ozler, Optimization for hot turning operations with multiple performance characteristics, Int. J. Adv. Manuf. Technol. 23 (2004) 777–782. [CrossRef] [Google Scholar]
M.S. Phadke, Quality engineering using robust design, Prentice Hall, New Jersey, 1989. [Google Scholar]
P.J. Ross, Taguchi techniques for quality engineering, McGraw-Hill, New Delhi, 1988. [Google Scholar]
S.R. Rajpurohit, D.M. Patel, Striation mechanism in laser cutting – the review, International J. Engineering Research and Applications (IJERA) 2 (2012) 457–461 [Google Scholar]
S. Chaki, R.N. Bathe, S. Ghosal, G. Padmanabham, Multi objective optimization of pulse Nd:YAG laser cutting process using integrated ANN-NSGAII model, J. Intell. Manuf. (2015), DOI: 10.1007/s10845-015-1100-2 . [Google Scholar]
A. Sharma, V. Yadava, Optimization of kerf quality using robust design of experiments during Nd:YAG laser cutting of thin Al alloy sheet for straight profile, Int. J. Mech. Eng. 1 (2011) 1–8. [Google Scholar]
T.A. El-Taweel, A.M. Abdel-Maaboud, B.S. Azzam, A.E. Mohammad, Parametric studies on the CO2 laser cutting of Kevlar-49 composite, Int. J. Adv. Manuf. Technol. 40 (2009) 907–917. [CrossRef] [Google Scholar]
R. Phipon, B.B. Pradhan, Control parameters optimization of laser beam machining using GA, Int. J. Comput. Eng. Res. 2 (2012) 1510–1516. [Google Scholar]
R. Miroslav, M. Milos, Experimental investigations of CO2 laser cut quality: a review, Nonconventional Technologies Review 4 (2011) 35–42. [Google Scholar]
R. Miroslav, D. Predrag, Research on surface roughness by laser cut, The Annals of University of Galati XII (2006) 41–88. [Google Scholar]
K. Huehnlein, K. Tschirpke, R. Hellmann, Optimization of Laser Cutting Processes Using Design of Experiments, Physics Procedia 5 (2010) 243–252. [CrossRef] [Google Scholar]
B. Adelmann, R. Hellmann, Fast Laser Cutting Optimization Algorithm, Physics Procedia 12 (2011) 591–598. [CrossRef] [Google Scholar]
A.P. Paiva, J.R. Ferreira, P.P. Balestrassi, A multivariate hybrid approach applied to AISI 52100 hardened steel turning optimization, Journal of Materials Processing Technology 189 (2007) 26–35. [Google Scholar]

Cite this article as : Gadallah MH & Abdu HM: Modeling and optimization of laser cutting operations. Manufacturing Rev. 2015, 2 , 20.

All Figures

	Procedure of Taguchi method [ ].

	Laser cutting machine utilized in this study.

	Schematic of laser cut kerf [ ].

	Effect of laser cutting parameters on S/N ratios ( ).

	Comparison of experimental and predicted results for kerf taper.

	Comparison of experimental and predicted results for surface roughness.

	Response surface plot of with power and oxygen pressure.

	Response surface plot of with power and frequency.

	Response surface plot of with power and cutting speed.

	Response surface plot of with pressure and cutting speed.

	Response surface plot of with frequency and cutting speed.

	Response surface plot of with power and pulse frequency.

	Response surface plot of with pressure and frequency.

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Finite element simulation and experimental assessment of laser cutting unidirectional cfrp at cutting angles of 45° and 90°.

1. Introduction

2. materials and methods, 2.1. experimental, 2.2. finite element simulation model, 2.2.1. transient thermal analysis.

The bulk and surrounding temperature was set to 28 °C.
Radiation heat loss was neglected.
Thermophysical material properties were assumed to be constant.
Pores or other material defects were neglected.
The laser beam energy distribution was uniform and ideal.

2.2.2. Geometry

2.2.3. material properties, 3. results and discussion, 3.1. temperature, 3.2. ablation depth, 3.3. heat affected zone, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

Sauer, M.; Schüppel, D. Market Report 2021—The Global Market for Carbon Fibers and Carbon Composites ; Composites United e.V.: Berlin, Germany, 2022. [ Google Scholar ]
Karataş, M.A.; Gökkaya, H. A review on machinability of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials. Def. Technol. 2018 , 14 , 318–326. [ Google Scholar ] [ CrossRef ]
Steen, W.M.; Mazumder, J. Laser Cutting, Drilling and Piercing. In Laser Material Processing ; Springer: London, UK, 2010; pp. 131–198. ISBN 978-1-84996-061-8. [ Google Scholar ]
Bluemel, S.; Jaeschke, P.; Wippo, V.; Bastick, S.; Stute, U.; Kracht, D.; Haferkamp, H. Laser Machining of CFRP Using a High Power Fiber Laser—Investigations on the Heat Affected Zone. In Proceedings of the 15th European Conference on Composite Materials, Venice, Italy, 24–28 June 2012. [ Google Scholar ]
Jaeschke, P.; Kern, M.; Stute, U.; Haferkamp, H.; Peters, C.; Herrmann, A.S. Investigation on interlaminar shear strength properties of disc laser machined consolidated CF-PPS laminates. Express Polym. Lett. 2011 , 5 , 238–245. [ Google Scholar ] [ CrossRef ]
Jaeschke, P.; Kern, M.; Stute, U.; Kracht, D.; Haferkamp, H. Laser processing of continuous carbon fibre reinforced polyphenylene sulphide organic sheets—Correlation of process parameters and reduction in static tensile strength properties. J. Thermoplast. Compos. Mater. 2014 , 27 , 324–337. [ Google Scholar ] [ CrossRef ]
Kononenko, T.V.; Freitag, C.; Komlenok, M.S.; Weber, R.; Graf, T.; Konov, V.I. Heat accumulation between scans during multi-pass cutting of carbon fiber reinforced plastics. Appl. Phys. A 2018 , 124 , 217. [ Google Scholar ] [ CrossRef ]
Kedir, N.; Hernandez, J.A.; Lim, B.H.; Gao, J.; Zhai, X.; Nie, Y.; Issahaq, M.N.; Tallman, T.N.; Chen, W.W. Effect of laser irradiation time on the surface characteristics of a carbon fiber composite. J. Laser Appl. 2021 , 33 , 042034. [ Google Scholar ] [ CrossRef ]
Parandoush, P.; Hossain, A. A review of modeling and simulation of laser beam machining. Int. J. Mach. Tools Manuf. 2014 , 85 , 135–145. [ Google Scholar ] [ CrossRef ]
Negarestani, R. Laser Cutting of Carbon Fibre-Reinforced Polymer Composite Materials. Ph.D. Thesis, The University of Manchester, Manchester, UK, 2010. [ Google Scholar ]
Sun, D.; Han, F.; Ying, W. Numerical simulation of water jet–guided laser cutting of carbon fiber–reinforced plastics. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2019 , 233 , 2023–2032. [ Google Scholar ] [ CrossRef ]
Yang, H.; Liu, H.; Gao, R.; Liu, X.; Yu, X.; Song, F.; Liu, L. Numerical simulation of paint stripping on CFRP by pulsed laser. Opt. Laser Technol. 2022 , 145 , 107450. [ Google Scholar ] [ CrossRef ]
Ohkubo, T.; Sato, Y.; Matsunaga, E.; Tsukamoto, M. Three-dimensional numerical simulation during laser processing of CFRP. Appl. Surf. Sci. 2017 , 417 , 104–107. [ Google Scholar ] [ CrossRef ]
Long, L.; Huang, Y.; Zhang, J. Experimental investigation and numerical simulation on continuous wave laser ablation of multilayer carbon fiber composite. Proc. Inst. Mech. Eng. Part L 2017 , 231 , 674–682. [ Google Scholar ] [ CrossRef ]
Moghadasi, K.; Tamrin, K. Multi-pass laser cutting of carbon/Kevlar hybrid composite: Prediction of thermal stress, heat-affected zone, and kerf width by thermo-mechanical modeling. Proc. Inst. Mech. Eng. Part L 2020 , 234 , 1228–1241. [ Google Scholar ] [ CrossRef ]
Xu, H.; Hu, J. Modeling of the material removal and heat affected zone formation in CFRP short pulsed laser processing. Appl. Math. Model. 2017 , 46 , 354–364. [ Google Scholar ] [ CrossRef ]
Bluemel, S.; Angrick, V.; Bastick, S.; Jaeschke, P.; Suttmann, O.; Overmeyer, L. Remote Laser Cutting of Composites with A Fibre Guided Thin-Disk Nanosecond High Power Laser. In Proceedings of the Lasers in Manufacturing Conference 2015, Munich, Germany, 22–25 June 2015. [ Google Scholar ]
Merker, G.P. Wärmeübergang Bei Laminarer Kanalströmung. In Konvektive Wärmeübertragung ; Springer: Berlin/Heidelberg, Germany, 1987; pp. 127–168. [ Google Scholar ] [ CrossRef ]
Keuntje, J.; Mrzljak, S.; Gerdes, L.; Wippo, V.; Kaierle, S.; Overmeyer, L.; Walther, F.; Jaeschke, P. Macroscopic simulation model for laser cutting of carbon fibre reinforced plastics. Procedia CIRP 2022 , 111 , 496–500. [ Google Scholar ] [ CrossRef ]
Sheng, P.; Chryssolouris, G. Theoretical Model of Laser Grooving for Composite Materials. J. Compos. Mater. 1995 , 29 , 96–112. [ Google Scholar ] [ CrossRef ]
Freitag, C. Energietransportmechanismen bei der Gepulsten Laserbearbeitung Carbonfaser Verstärkter Kunststoffe. Ph.D. Thesis, Universität Stuttgart, Stuttgart, Germany, 2017. [ Google Scholar ]
Puck, A. Festigkeitsanalyse von Faser-Matrix-Laminaten: Modelle für die Praxis ; Hanser: München, Germany, 1996. [ Google Scholar ]
Staab, G.H. Laminate analysis. In Laminar Composites , 2nd ed.; Butterworth-Heinemann: Oxford, UK, 2015; pp. 189–284. ISBN 978-0-12-802400-3. [ Google Scholar ]
Adibekyan, A.; Kononogova, E.; Monte, C.; Hollandt, J. Review of PTB Measurements on Emissivity, Reflectivity and Transmissivity of Semitransparent Fiber-Reinforced Plastic Composites. Int. J. Thermophys. 2019 , 40 , 36. [ Google Scholar ] [ CrossRef ]
Bluemel, S.; Staehr, R.; Jaeschke, P.; Suttmann, O.; Overmeyer, L. Correlation of internal and surface temperatures during laser cutting of epoxy-based carbon fibre reinforced plastics. J. Reinf. Plast. Compos. 2015 , 34 , 662–671. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Material Property	Carbon Fibre	Laminate
Density,		1575
Specific heat capacity,		982.7
Thermal conductivity,	║50
Thermal diffusivity,	║ ┴	║ ┴
Evaporation temperature,

	No.
Experiment	E1	45
	E2	45
	E3	90	1
	E4	90	5
Simulation	S1	45		43.38	43.38
	S2	45		198.79	38.14
	S3	90	1	43.83	43.83
	S5	90	5	212.39	41.87

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Keuntje, J.; Mrzljak, S.; Gerdes, L.; Wippo, V.; Kaierle, S.; Walther, F.; Jaeschke, P. Finite Element Simulation and Experimental Assessment of Laser Cutting Unidirectional CFRP at Cutting Angles of 45° and 90°. Polymers 2023 , 15 , 3851. https://doi.org/10.3390/polym15183851

Keuntje J, Mrzljak S, Gerdes L, Wippo V, Kaierle S, Walther F, Jaeschke P. Finite Element Simulation and Experimental Assessment of Laser Cutting Unidirectional CFRP at Cutting Angles of 45° and 90°. Polymers . 2023; 15(18):3851. https://doi.org/10.3390/polym15183851

Keuntje, Jan, Selim Mrzljak, Lars Gerdes, Verena Wippo, Stefan Kaierle, Frank Walther, and Peter Jaeschke. 2023. "Finite Element Simulation and Experimental Assessment of Laser Cutting Unidirectional CFRP at Cutting Angles of 45° and 90°" Polymers 15, no. 18: 3851. https://doi.org/10.3390/polym15183851

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Modeling Of Steel Laser Cutting Process Using Finite Element, Machine Learning, And Kinetic Monte Carlo Methods

Laser cutting is a manufacturing technology that uses a focused laser beam to melt,burn and vaporize materials, resulting in a high-quality cut edge. Although previous efforts are primarily based on a trial-and-error approach, there is insufficient understanding of the laser cutting process, thus hindering further development of the technology. Therefore, the motivation of this thesis is to address this research need by developing a series of models tounderstand the thermal and microstructure evolution in the process.

The goal of the thesis is to design a tool for optimizing the steel laser cutting processthrough a modeling approach. The goal will be achieved through three interrelated objec-tives: (1) understand the thermal field in the laser cutting process of ASTM A36 steel using the finite element (FE) method coupled with the user-defined Moving Heat Source package;(2) apply machine learning method to predict heat-affected zone (HAZ) and kerf, the keyfeatures in the laser cutting process; and (3) employ kinetic Monte Carlo (kMC) simulationto simulate the resultant microstructures in the laser cutting process.

Specifically, in the finite element model, a laser beam was applied to the model with the parameters of the laser’s power, cut speed, and focal diameter being tested. After receiving results generated by the finite element model, they were then used by two machine learning algorithms to predict the HAZ distance and kerf width that is produced due to the laser cutting process. The two machine learning algorithms tested were a neural network and asupport vector machine. Finally, the thermal field was imported into the kMC model as the boundary conditions to predict grain evolution’s in the metals.

The results of the research showed that by increasing the focal diameter of a laser, the kerf width can be decreased and the HAZ distance experienced a large decrease. Additionally, apulse-like pattern was observed in the kerf width through modeling and can be minimized into more of a uniform cut through the increase of the focal diameter. By increasing thepower of a laser, the HAZ distance, kerf width, and region of the material above its original temperature increase. Additionally, through the increase of the cut speed, the HAZ distance, kerf width, kerf pulse-like pattern, and region of the material above its original temperature decrease.

Through the incorporation of machine learning algorithms, it was found that they can be used to effectively predict the HAZ distance to a certain degree. The Neural Networkand Support Vector Machine models both show that the experimental HAZ distance datalines up with the results derived from ANSYS. The Gaussian Process Regression HAZ model shows that the algorithm is not powerful enough to create an accurate prediction. Additionally, all of the kerf width models show that the experimental data is being overfit by the ANSYS results. As such, the kerf width results from ANSYS need additional validation to prove their accuracy.

Using the kMC model to examine the microstructure change due to the laser cutting process, three observations were made. First, the largest grain growth occurs at the edge ofthe laser where the material was not hot enough to be cut. Then, grain growth decays as thedistance from the edge increases. Finally, at the edge of the HAZ boundary, grain growth does not occur.

Degree Type

Master of Science in Mechanical Engineering
Mechanical Engineering

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Indianapolis

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https://www.ijert.org/experimental-analysis-of-laser-cutting-machine https://www.ijert.org/research/experimental-analysis-of-laser-cutting-machine-IJERTV10IS040093.pdf LASER(Light Amplification by Stimulated Emission of Radiation) cutting is one of the best technology developed for the cutting, drilling, micro machining, welding, sintering and heat treatment. It is one of the thermal energy based unconventional process used for cutting of complex profile materials with high degree of precision and accuracy. All cutting parameters have significant influence on quality work. The aim of this study is to relate the CO2 laser cutting parameters such as laser power and cutting power. The laser beam is typically 0.2mm in diameter with a power of 1-10 kW. Depending on the application selection of different gases are used in conjunction with cutting. Increasing the frequency and the cutting speed, decrease the kerf width and the roughness of cut surface, while increasing the power and gas pressure increases the kerf width and roughness. The relation between the input parameters and the response were investigated. The performance of laser cutting process mainly depends on laser parameter. Laser has been an important tool in the modern industries. Due to its unique properties such as high power density , monochromaticity, coherency and directionality laser has a wide variety of application. It application involves in material processing, medicine, research and development, communications and measurements to name a few. The attractive features of laser machining includes:-Narrow Kerf width and material saving-narrow heat affected zone and low thermal distortiom-non contact process and no tool wear-soft tooling and simple fixturing-easy automation

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Professor Mohamed Hassan Mehany H . Gadallah , Eng.hany mohamed

ABSTRACT Taguchi's parameter design is a systematic approach to optimize process performance, quality and cost. Laser beam cutting (LBM) is a non-traditional machining process widely used for cutting, drilling, marking, welding, sintering, and heat treatment. The objective of this study is to apply Taguchi optimization methodology to optimize Laser beam cutting parameters of Stainless steel (316L) to achieve optimal Average Kerf Taper (Ta), Surface Roughness (Ra) and Heat affected zone (HAZ). A series of experiments are conducted using (LBM) to relate machining parameters to several quality responses. Analysis of variance (ANOVA), Analysis of mean (ANOM), Orthogonal array (L27OA) and signal to noise ratio are employed to analyze the influence of process parameters. The machining parameters are machining on power (Watt), oxygen pressure (MPa), pulse frequency (Hz) and cutting speed (cm/min). Another objective is to build mathematical models for average kerf taper and average surface roughness as function of significant process parameters using Response Surface Methodology (RSM). Experimental results for both S/N ratio and mean response values show that power, oxygen pressure, and cutting speed are the most significant parameters that influence Kerf taper at confidence levels 99%, 95%, and 90% respectively. On the other hand, power and oxygen pressure are the significant parameters that influence average surface roughness at confidence levels 99%95%, and 90% respectively, consequently both the power and pressure of oxygen are the criteria that affect the impact of the heat affected zone at confidence levels 99%, 95%, and 90% respectively. RSM models are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA) via optimization toolbox MATLAB. Optimum solutions are compared with Taguchi Methodology results. Further validation experiments are carried to verify developed models with success.

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Laser cutting is mostly a thermal process in which a focused laser beam is used to melt material in a localized area. A co-axial gas jet is used to eject the molten material from the cut and leave a clean edge. A continuous cut is produced by moving the laser beam or work piece and leave a clean edge. A particular characteristic of a laser cut is the formation of striations on the cut edge. These striations play an important part in laser cutting as they effectively control the edge roughness. Laser Beam Machining is widely used manufacturing technique utilized to perform cutting, engraving and welding operations on a wide variety of materials ranging from metals to plastics. In the present work an attempt has been made to study the effect of process parameters such as feed rate, input power and standoff distance on the quality of the machined surface using laser beam on mild steel and stainless steel. The quality of cut is assessed in terms of response parameters such as upper kerf...

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Laser Physics

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Industrial Energy Optimisation: A Laser Cutting Case Study

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Published: 28 September 2023
Volume 11 , pages 765–779, ( 2024 )

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Nicholas Goffin ORCID: orcid.org/0000-0003-0728-3704 1 ,
Lewis C. R. Jones ORCID: orcid.org/0000-0002-6413-4599 1 ,
John R. Tyrer ORCID: orcid.org/0000-0002-0003-9496 1 ,
Jinglei Ouyang ORCID: orcid.org/0000-0002-9583-6702 2 ,
Paul Mativenga ORCID: orcid.org/0000-0002-7583-5163 2 ,
Lin Li ORCID: orcid.org/0000-0003-2627-2517 2 &
Elliot Woolley ORCID: orcid.org/0000-0002-5445-4687 1

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In an increasingly technological world, energy efficiency in manufacturing is of great importance. While large manufacturing corporations have the resources to commission energy studies with minimal impact on operations, this is not true for small and medium enterprises (SME’s). These businesses will commonly only have a small number of laser processing cells; thus, to carry out an energy study can be extremely disruptive to normal operations. Since rising global energy costs also have the largest impact on small businesses who lack the benefit of economies of scale, they are simultaneously the most in need of improvements to energy efficiency, while also facing the strongest practical barriers to implementing them. In this study, a laser processing energy analysis methodology was designed to run simultaneously with normal operation and applied to a laser shim-cutting cell in a UK-based SME. This paper demonstrates the methodology for identifying operating states in a production environment and Specific Energy Consumption and Scope 2 CO 2 emissions results are analysed. The Processing state itself was the most impactful on overall energy performance, at 55% for single sheets of material, increasing to 71% when batch processing. Generating idealised data in this production environment is challenging with restrictions to isolating variables, these “real-world” limitations for conducting system energy analysis simultaneously with live production are also discussed to present recommendations for further analysis.

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1 Introduction

Industrial laser processing faces a challenge in energy efficiency and sustainability. Energy costs are steadily growing, and increasing awareness of resource depletion and environmental impact drives increased interest in energy saving [ 1 ]. Given the significant growth rate of the laser market, expected to reach a global value of $14.52 billion by 2026 [ 2 ], the scale of this challenge will only increase.

1.1 Energy Saving in Laser Processing

Lasers are often presented as a low-energy alternative to other manufacturing processes. For example, Lisco et al . showed that laser processes have the capability to save energy in the thermal annealing of thin-film photovoltaic panels [ 3 ]. The use of lasers in additive manufacturing is well-known for its ability to promote energy and cost savings, as described by Ford et al . [ 4 ] along with material optimisation in design. Although Peng et al. [ 5 ] have recognised that more work is required to fully realise the sustainability potential of additive manufacturing. Therefore, in the first instance, the adoption of laser-based processes can itself be understood as an energy-saving measure.

When investigating laser processing, energy-saving studies have historically focused on optimising the beam-material interaction via several different approaches. The use of preheated wires to reduce energy consumption in laser welding achieved energy reductions of 16% [ 6 ]. Other experiments have investigated wire shaping in laser Directed Energy Deposition (DED) by Goffin et al . [ 7 ], and laser beam shaping by Wellburn et al . [ 8 ] and Mok et al . [ 9 ]. There have also been many system-level investigations of different laser processes. Kellens et al. investigated laser cutting [ 10 ], by identifying the major sub-systems involved (laser, cooling, motion, exhaust). They measured these independently and evaluated their environmental impact, generating a map of the environmental impact of specific cuts and recommendations for improvement. Ouyang et al . [ 11 ] took a similar approach and applied this assessment to laser cleaning, with an intercomparison of several different types of laser cleaning systems. In this case, the energy modelling of the sub-systems was related directly to Scope 2 CO 2 emissions.

Sub-system breakdowns of laser systems are application-specific. For example, analyses of powder-bed additive manufacturing by Baumers et al . [ 12 ] included powder-recoating systems, which are not relevant for laser cutting. For Direct Laser Metal Deposition (DLMD), Selicati et al. [ 13 ] demonstrated a complete sub-system exergy-based analysis that included capturing the mass balance of blown powder.

1.2 Previous Industrial Case Studies in Manufacturing Energy Saving

Energy audits have played a significant role in identifying barriers to energy efficiency. For example, Chiaroni et al . [ 14 ] evaluated the entire operations of an American home appliances company. A large amount of electricity was saved by replacing older lightbulbs with low-power LED versions, upgrading to more efficient motors and adding thermal insulation to presses to reduce warm-up time. Their method allowed the quantification of energy saving and the economic viability of the recommended measures, which helped to overcome barriers such as lack of interest, prioritisation and even how to identify inefficiencies in the first place. A review of industrial case studies by Worrell et al . [ 15 ] looked at energy savings measures in a number of different manufacturing industries: food, building materials, steel, paper, chemicals and textiles. This study evaluated energy savings and productivity improvements, with an average energy payback period of 4.2 years, which was reduced to 1.9 years once non-energy benefits were accounted. While these studies were effective, they were aimed at facility-wide systems, such as air conditioning, lighting and thermal insulation. They did not include a detailed energy analysis of the manufacturing processes themselves.

More specific case studies have been conducted where manufacturing energy analysis has been applied to manufacturing processes. Like the energy modelling discussed earlier, these have evaluated various approaches. Cai and Lai [ 16 ] conducted a sustainability analysis of mechanical manufacturing systems in an industrial facility to address an apparent gap in the literature regarding sustainability benchmarking for these types of systems. This benchmarking approach was then successfully applied to a small manufacturing enterprise in China.

Gadaleta et al . [ 17 ] modelled the physical placement of industrial robots within an automotive manufacturing facility to minimise their energy requirements. This involved computational modelling of the capabilities and energy usage of the specific robots (Kuka, in this case) under consideration and culminated in physical testing, which gave energy savings of approximately 20%. Modern Computer Integrated Manufacturing tools have assisted with this; for example, Vatankhah et al. [ 18 ] used digital twinning to optimise the tool paths of a Kuka robot, reducing energy consumption by 7.7%, and Le et al . [ 19 ] carried out energy monitoring of injection moulding and metal stamping. For injection moulding, only about 1/3 of the energy was used for creating products. 63% was used for other states; warm-up, idle, start-up, switch-off and pump heating. For metal stamping, this is reversed, with 62% of energy being used for stamping and 38% unproductive (idle/off). Subsequently, Madan et al . [ 20 ] developed a guideline for the energy performance evaluation of injection moulding, incorporating the process state and accounting for the performance of individual sub-systems. Their work also included the development of a graphical user interface (GUI) to assist the user.

A comparison was carried out between additive-subtractive “hybrid” manufacturing and traditional subtractive machining by Liu et al . [ 21 ] to enable selection between them. Selection depended on where in its lifecycle the part consumed the most energy. The best way to optimise the carbon footprint for a part manufactured by the hybrid method was in design, e.g. via lightweighting. By contrast, the optimal way to optimise the carbon footprint for subtractive machining was by material recycling or some other method of reducing the energy of producing the raw material.

Industrial case studies have also been carried out specifically for laser processing. Morrow et al . [ 22 ] investigated the potential environmental benefits of laser-based Directed Energy Deposition (DED) processes for manufacturing tools and dies. They found that while the trade-offs were complicated, and DED was not beneficial in all cases, improvements were driven by the solid:cavity ratio, with DED having advantages when the ratio was low with minimal finishing required. Design improvements allowed by DED processes were also found to give potential benefits for moulds in use, reducing cycle time by 10–40%. Following this, Guarino et al . [ 23 ] compared the environmental impact of laser cutting vs selective laser melting (SLM) for manufacturing stainless steel washers. This included manufacturing the source material, powder for SLM and rolled sheet for laser cutting. Overall, SLM gave superior mechanical strength but inferiority in every other category, with an environmental impact approximately 2.5 times greater than laser cutting.

The use of metrics and models for Material Removal Rate and Specific Energy Consumption have been used in experimental studies of mechanical machining [ 24 , 25 , 26 ]. However, there is no single established methodology for energy efficiency improvement in machine tools. It has been identified that these metrics can be used to produce benchmark-based energy assessment of manufacturing [ 27 , 28 ]. These analyses have not been applied to laser based manufacturing processes.

A limitation of manufacturing energy studies is that previously it has been assumed that the machine under investigation is available for study. This is true in larger manufacturing facilities; however, it does not necessarily apply to Small and Medium-sized Enterprises (SMEs), which may be unable to take a machine offline to carry out specialised energy investigations or even have a single machine. Since many laser companies in the UK are SMEs (around 400 individual companies with a total annual turnover of £500 million [ 29 ]), this is a significant issue to address. Bjørnbet et al . [ 30 ] reviewed manufacturing case studies and identified two primary needs for industry. Firstly, there is a need for a solid empirical research base and the ability to quantify the effects. Secondly, there is a need to report failures and limitations of studies and not limit reporting only to success stories.

1.3 Investigation Objectives

The research question under consideration is “How does the energy consumption of laser cutting in an industrial environment differ from that in a research environment?”. This question will address limitations identified in the literature through the following two objectives:

Include other operating states in the investigation beyond Processing covered previously and identify the transition of states and variables that impact industrial energy performance that were not apparent in a laboratory environment.

Develop analysis from energy (e.g. kJ) to output CO 2 emissions (e.g. kg CO 2 ). While energy is the variable being measured, emissions reduction is the ultimate requirement.

The method used to generate this laser-cutting case study was not developed from a design of experiment to evaluate specific parameters but uses existing procedures and equipment from an in-use commercial system. This was chosen to address the recommendations of Bjørnbet et al . [ 30 ]., however, this creates a more constrained experimental environment but provides industrially relevant data. Successful and unsuccessful aspects are presented, along with recommendations for future studies. These limitations provide valuable insights.

2.1 Industrial System and Measurement Equipment

An SME manufacturing job shop that specialises in laser cutting processes was used for this case study. The chosen process measured was the precision laser cutting of thin sheet (0.2–0.5 mm thickness) material. This is a common process for laser cutting equipment used to manufacture batches of components such as shims, washers, and spacers where higher tolerances and edge quality is often required. This company uses a single laser cutting machine to produce these products for customers, and the experiment would measure the energy consumption of this system over a single day of production.

The manufacturing system was a Trumpf TruLaser Cell 3000, equipped with a 2 kW disk laser, wavelength 1030 nm (see Fig. 1 a). This comprised three distinctly powered pieces of equipment: The laser source , which would account for all electrical input to the laser required to produce the optical output; the laser chiller , which provided coolant water to the laser source and electrical input was used to power separate pump and refrigerator components; the c ell incorporated the computer control, CNC motion, motion cooling, extraction and safety sub-systems.

Image showing a the external view of the TruLaser Cell 3000, 3 phase power connection to b laser source (power phases labelled), c laser chiller, and d cell

A MultiCube 950 energy monitor (NewFound Energy Limited, UK) was used to measure the power of each of these three connections simultaneously. Current transformers were attached to the individual phases of each piece of equipment (see Fig. 1 b–d). The energy monitor is capable of measurement rates of up to 1 data point per second, calculated from 1200 individual sub-samples per second, with a maximum current of 50 A and a maximum voltage of 230 V on each phase. The device was specified as Class 0.25 for kW and kVA measurements according to BS EN 60688:2013, giving an accuracy of ± 0.25%.

Over the course of the planned observation period, six distinct products were produced using the laser cutting equipment. Although each product has its own characteristics, they all involve laser cutting of thin sheet material, making them a comparable set of cutting operations in a real-world scenario. Images of a selection of the individual products are given in Fig. 2 . For commercial reasons, it was not possible to photograph all products but all products are 2D geometry cut from the thin sheet, and energy monitoring data was collected for the six products. Details of each product are given in Table 1 . In all cases, laser power was set at 300 W. Each run produced a number of components manufactured from a single sheet of material loaded into the machine.

Images showing a Product 1, b Product 2, c Product 3, and d Product 5. Note that images of products 4 and 6 are not included due to the commercial nature of the parts

2.2 Energy Analysis

A methodology for measuring and calculating the Specific Energy Consumption (SEC) (J/kg) for a laser process which incorporates the direct energy-consuming sub-systems and operating states of a complete laser system has been developed and demonstrated in a research environment [ 31 , 32 ]. The subject of this paper is the adaption of that existing methodology to an industrial case study.

The following operating states were identified for the system and the power consumption of each sub-system will be determined at each state.

Off: All sub-systems are inactive and drawing no power.

Warm-up: Request for all sub-systems to be active. There may be a period when first turned on for sub-systems to become operational.

Idle : All sub-systems active, with the system ready to process but not performing its cutting function.

Processing : All sub-systems active, with the system performing its cutting function.

The manufacture of the six products will be monitored for the duration of the process. The duration of each operating state will also be recorded by an observer so that the events can be attributed to the energy data. The data from the energy meter will be combined into a single plot of the electrical power for the entire laser system. The power consumption of each operating state will be compared to assess the overall efficiency of the machine.

To evaluate the productivity of the process, firstly, the SEC will be calculated for the six products:

where P operating states is the apparent power of each operating state, t operating states is the time of each operating state, and m c is the mass of material cut.

The mass was determined by the removed volume:

where d is the depth of the cut equal to the material thickness, k is the kerf width, l is the total length of the cut, and ρ is the density. Density was assumed to be 7700 kg/m 3 for all samples.

Secondly, the processing rate, PR , in kg/h can be calculated:

where t c is the total cutting time.

Previous research [ 33 , 34 ] has shown an empirical relationship between SEC and PR that follows an inverse curve relationship. Equation 3 presents the unit process energy consumption model

where C 0 and C 1 are process-specific constants. C 1 represents the effective work done by the machine and will be proportional to the processing parameters. C o is independent of processing parameters and represents the basic energy required for running the system by all operating states. Equation 5 represents the contribution of the operating states to the process-specific constants in the unit process energy consumption model.

where n is the number of times that each operating state appears for each individual process.

Processing is the only operating state associated with the PR. Other states contribute to the system SEC but do not contribute to production. IBM SPSS was used to perform an inverse curve regression analysis to determine the process-specific constants. This was performed to determine if there was a valid relationship and to compare this process to others reported in the literature [ 35 ].

The electrical power measured can be converted to evaluate the Scope 2 indirect carbon emissions from the cutting process. Different electrical generation technologies generate different levels of carbon emissions. The average carbon intensity of electricity generation covering the entire national electrical generation mix for the UK in 2021 was 0.21016 kg CO 2 /kWh [ 36 ]. The energy used can be converted by 5.84 × 10 –5 kg CO 2 /kJ.

The power consumption was monitored for this system over a complete day of work, 16 h, to identify each sub-system’s operating states and power consumption. The manufacture of 6 products was monitored during this time period. The overall apparent power draw for the six products is shown in Fig. 3 . These were individually analysed to identify the power of each operating state and sub-system consumption.

Apparent Power for the six observed products

In the industrial SME context, the system is only off when the business is closed and for any servicing of the machinery, which was not observed. Alterations to the Off operating state, therefore, would require consideration of the organisational structure and scope of the business and were considered outside the scope of this investigation.

The Warm-up state represents a transition from Off to Idle, occurring once per shift cycle. Like the Off state, it is not directly responsible for producing any product but does consume energy. However, the Warm-up state for this specific laser source lasts 10 min, compared to the total time of 16 h for the Idle and Processing states, representing only 1% of the total.

Figure 4 shows an example plot of the combined power draw of the complete system when manufacturing a product, with the individual sub-systems labelled. It was necessary to separate the power draw of the laser chiller and CNC chiller into their base load of pumping and the additional power consumption when cooling. The key identified features

Activation spike: An initial power spike was measured for both the cell and the laser chiller when activated.

CNC chiller cooling: The CNC system in the cell had its own individual chiller. This chiller created a small additional power draw when it was actively cooling, as opposed to only pumping the coolaroundound the system.

Laser chiller cooling: This generated a significant additional power draw when the laser chiller was actively cooling, vs. simply pumping coolant round the system.

Laser source + CNC motion + chiller pumping + computer control + extraction + safety: The rest of the sub-systems provide a constant load to the system. During the Processing operating state, the system cutting speed was so fast that it switched between motion only (repositioning between cuts) and motion and cutting together more quickly than the energy monitor could capture. This appears as a small amplitude higher frequency fluctuation in the measurement data.

Plot of overall system power for Product 3 Processing state with identification of individual sub-systems

The two states of primary interest were the Idle state and the Processing state. The system was found to be Idle for three reasons:

Configuring : A new job is started. This requires the operator to remove the old materials and load new ones and load the new program

Change-over : Within a given job, more than one sheet may be used. This requires the operator to unload and load new material.

Waiting: This covers idle time where the operator is absent, e.g. for lunch breaks, meetings etc.

This lead to the operating state flowchart in Fig. 5 . Thus, for an individual laser cutting job, the Off and Warm-up states would not be expected to occur at all, there would be one initial Idle (configuring) state to set the job up, and then multiple recurring Processing and Idle (change-over) states, until the job is complete, dependent on the number of individual sheets required.

Flowchart of operating states in industrial laser cutting, showing how they interact and the different types of Idle state

4 Productivity Analysis

Four initial operating states were identified for this system, adapting the states identified by Goffin et al . [ 31 ], derived from operating states defined in BS ISO 14955–1:2017 [ 37 ]. Table 2 shows the average machine power and energy draws when changing material or loading a new job. Since the system is in the same power state on both occasions, the Overall system energy requirement or consumption is dependent on the time taken.

The energy use of the Idle state compared to the Processing state is therefore dependent not only on the relative times and power draws of the different states but also on the number of times the state occurs within a given process. The Idle (configuring) state occurs only once per product type, but could occur several times over a single shift, whereas the Idle (change-over) could be expected to occur multiple times per product type in order to reload the machine is material to produce an increased number of product.

To analyse this energy use, Fig. 6 shows the relative energy draws of the Idle (configuring), Idle (change-over) and Processing states when only a single sheet of raw material is required and when a 5-sheet batch of raw material is required. This analysis compares the experimental data from a single sheet and calculates the estimated energy usage if a larger quantity of product was ordered. This analysis compares the relative percentage of each operating state and estimates their effective change with an increase in production volume. The increased number of sheets increases the relative proportions of the Idle (change-over) energy and Processing energy, and the Idle (configuring) energy decreases.

Relative energy requirements for products using 1 sheet of material and an estimated production job requiring 5 sheets of material to be loaded into the system

While the overall trend is similar to previous work [ 38 ], in that it is an inverse curve, the correlation is much weaker. This is reflective of the fact that whereas the previous research was carried out in a laboratory environment with a variety of parameters for each individual process, each of the cutting processes here also used different materials, thicknesses, kerf widths and geometry. The inverse curve model has previously been shown to be good at providing a relationship between parameters on the same process but not valid for estimating different process, which in this case are different material substrates (Table 3 ).

Figure 7 compares the productivity plot for industrial laser cutting against the previously published data for laboratory-based laser welding [ 32 ] (Sig.: C 1 = 0.000, C 0 = 0.000). The industrial laser cutting data has less statistical significance than the previous lab-based laser welding work, primarily to the much lower quantity of data (6 data points compared to 24). However, Fig. 7 shows similarity when the two are plotted together. The Processing rates for the two processes overlap, while the Specific energy consumptions are within 0.5 orders of magnitude. This suggests that while the industrial data is a smaller sample compared to than the lab-based data, there is similarity between the two.

Processing rate compared to Specific energy consumption for this industrial laser cutting analysis and previous lab-based laser welding analysis

5 Emissions Analysis

This emissions data was used to calculate the emissions output of the different cutting processes, for both 1 sheet and a batch of 5 sheets. The results are shown in Fig. 8 .

Carbon emissions from cutting products for a single sheet and batch of five sheets

In all cases, the majority of emissions come from the cutting state, which is proportional to process time, both per sheet and with the number of sheets. Table 4 shows that the ratio between Processing emissions and Idle (changeover) emissions stays fixed, independent of the number of sheets used, whereas the proportion of the Idle (configuring) state reduces when the number of sheets increases.

Both Idle states are process dependent since their proportion of the total emissions output changes according to the specific process. However, the Idle (configuring) state is also affected by process time since this state occurs only once per process. Therefore, as the Processing time increases, both the Processing and Idle (change-over) states multiply, but the Idle (configuring) state does not and proportionally reduces as a result.

6 Discussion

This work has further extended and developed the welding energy modelling work previously published to industrial laser cutting, now accounting for various operating states and adding consideration of Scope 2 CO 2 emissions.

6.1 Energy Consumption

In this work, the statistical modelling derived by Goffin et al . [ 32 ] was applied to an industrial laser cutting process. The most immediate consequence of this was a significant reduction in the number of available data points for statistical analysis from 21 to 6. The productivity analysis showed that when the industrial data from this case study was plotted on the same axes as the previous laboratory-based data, their processing rates overlapped, with the specific energy consumptions within 0.5 orders of magnitude of each other. This shows that the case study data is realistic, even if not of sufficiently high quantity for a full statistical analysis.

Previous welding research selected a single welding process with a specified material and tool path and then investigated an array of parameters. In this case study, multiple cutting processes were studied, representing different products and materials, at individual fixed parameters. This large range of different products, with their individual total cut lengths, geometries and sub-geometries, as shown in Fig. 2 , explains the low level of statistical significance. Table 4 shows that the statistical variation is heavily contributed to by the Idle C 0 constants, which both have large coefficients of variation. At the present time, the standard deviation in the results is too large, and the p-value too small, for the model to be statistically meaningful.

In Fig. 9 a detailed comparison can be observed between the laser system productivity analysis and other manufacturing processes. The comparison presents a contrast between traditional manufacturing techniques and the latest data generated. The mean and range SEC and PR values for the laser cutting system have been superimposed on the original manufacturing process analysis by Gutowski et al. [ 19 ]. This further demonstrates that the system is processing material at the expected rate for its specific energy consumption. Laser processing is situated within the constant gradient portion of Gutowski et al.’s analysis. Often lasers are assumed to be substantially more efficient than the conventional processes they are replacing but the high specific energy requirements of the whole system when accounting for more than just the Processing state shows that they may have high material feed rates but are energy intensive. This demonstrates the useful potential for a consistent machine benchmarking tool [ 27 ].

Processing rate compared to specific energy consumption various manufacturing processes. Adapted with permission from Gutowski et al . [ 35 ] with original legend and citations.

The research carried out here demonstrated the ability to gather a snapshot of the overall range within which the process operates, which is consistent with previous research that showed a negative correlation and an approximate 2-orders-of-magnitude range in results. However, more complete data is required to establish the statistical significance of the productivity equation and use it predictively. The next logical step would be to gather data specific to individual processes at ranges of different parameters.

Work carried out in R&D contexts often treats process states as a linear progression from start to finish, as with Ouyang et al. [ 39 ] for laser decoating. In an industrial context, as shown in Fig. 5 , the relationships between the states are not linear; some states occur more than once, and others have minimal practical impact. The Off and Warm-up states can be expected to occur rarely (e.g., once per shift or after downtime), with Idle and Processing states alternating multiple times. This means that there are four states of primary interest to the investigator; Idle (configuring), Idle (change-over), Idle (waiting) and Processing. Process setup only occurs once, so the energy proportion of this state reduces as the process gets longer. The Idle (change-over) and Processing states increase together as the number of sheets increases, so the percentage of Idle (change-over) time increases at the same rate as the percentage of time for Processing.

This concurs with Kellens et al. [ 40 ], whose time study distinguished between productive and non-productive states, for power-bed additive manufacturing. When adapted to this case study, the Off, Warm-up, Idle (configuring) and Idle (waiting) states are identified as non-productive, while the Processing and Idle (change-over) states are identified as productive. Therefore, the longer the processing time per sheet, the more energy efficient the process is. There are several ways to optimise this:

Minimisation of Idle (configuring) time: Efficient stocking of material, manual aids and other measures designed to reduce the time the machine spends idle in between processes.

Minimisation of instances of Idle (change-over): Use of large sheets of material to reduce the number of times that material changeover is required. This is subject to the limitations of the machine, and can affect Point 1, since a larger sheet can cause loading difficulties.

Optimisation of parts per sheet: Efficient nesting of parts on a sheet to reduce the number of sheets, another method to reduce instances of the Idle (change-over) state.

Reduction of energy consumption during Idle states: Measures taken to reduce machine power draw during the Idle state, e.g. disable or reduce coolant pumps.

Optimisation of processing parameters: Previous research has shown that maximising process parameters can be optimised to reduce the energy input per unit of processed mass. This acts to reduce the energy consumption of the Processing state.

The scope of the energy investigation can be set by defining states as either Operational or Physical; Physical states being those driven by the laser process itself, and Operational states driven by the way the organisation operates. In this investigation, Operational Idle states were not investigated, but have been identified as required for inclusion. Identification of the cause is important to account for the difference between scheduled (e.g., lunch break) and unscheduled (e.g., equipment failure) time. Overall Idle (Waiting) would then be depreciated amongst the individual Processing states, so each processing state has an assigned proportion of the Idle (Waiting) time. This would allow the effect on the productivity of the Idle (Waiting) state to be characterised.

For operational states, measures will be organisational. There will be differences in the duration of certain operating states dependent on individual operators. For example, the idle operating states will be unique to operators based on how they perform this function. The time for change-over operations should be kept to a minimum, but due to the limited observation window for this study of a single machine and operator, no variance in this procedure has been captured by this analysis and should be included in further studies. Larger periods of time the machine spends idle during lunch breaks will depend on the length of lunch breaks and whether there are operators who can keep the machine operating during that time. When the machine is Off, it is producing no product but also consumes no energy. To be reactivated, it has to go through its warm-up state again, therefore Off and Warm-up can be paired since they always occur together. The energy cost of leaving the machine idle can be compared to the energy cost of deactivating and then reactivating.

From Table 2 , Idle power is 5.64 kVA. Total energy for the Warm-up state is 2600 kJ. This implies that if Idle states are to exceed 7 min 41 s it is more energy efficient to shut down the machine and reactivate it when needed. From an operational standpoint, this means that the machine should be left idle for activities such as correcting programming errors and collecting materials but deactivated for longer periods such as lunch breaks. Consideration would need to be given to the policies and practical measures required to implement this (e.g., for someone else to reactivate the machine 10 min before work restarts).

6.2 Emissions

In Fig. 8 , the majority of emissions are generated by the Cutting State. While productivity analysis showed that minimisation of the Idle state is required to maximise process energy efficiency, emissions analysis shows that optimisation of the cutting state is also required. For an SME manufacturing facility, this cannot involve hardware changes since the cutting equipment is bought “off the shelf” from the manufacturer (in this case, Trumpf). Therefore, identifying the energy draws of the different sub-systems (as discussed in Sect. 1.1 ) is not of any real practical value when determining the best course of action for the machine operator to reduce the emissions of the Cutting state.

Since this was intended as a “fly on the wall” investigation to understand how to characterise process energy in an industrial context, no parameter experimentation was carried out. However, that would be the next logical step. Previous parameter experimentation [ 32 ] indicates that considerable energy savings can be realised when processing parameters are optimised for energy efficiency, with a 60% saving identified for laser welding. The potential benefits of applying such an investigation to laser cutting are therefore also considerable.

This leads to the non-technical barriers to sustainability adoption identified by Chiaroni et al . [ 14 ]. Put simply, a machine being used to optimise cutting parameters is a machine that is not in use producing parts for customers. If it is the only machine available for its specific task, an investigation could put a halt to production entirely. It becomes a business operations question, not an engineering one. As such, management buy-in is required to accept the short-term loss in order to realise the long-term gain, which could be problematic since the short-term loss and disruption is potentially significant. A potential solution is to make use of scheduled downtime to carry out investigation. Since the equipment is non-productive during this time anyway, with concomitant disruption to the usual schedule, parameter optimisation investigations can be carried out with a minimum of additional disruption.

7 Conclusions

In this investigation, a commercial laser cutting machine was investigated in an industrial setting. Building on previous work, the investigation included operating states previously discounted and expanded to incorporate Scope 2 CO 2 emissions.

The following conclusions, based on the research questions, have been drawn from this work:

The inherent limits of the non-invasive investigation makes collection of high-quality data more difficult than in a controlled laboratory. This has a significant effect on the sample size for mathematical modelling, and greater attention should be paid to this in future work.

Comparison with previous work in Fig. 7 shows continuity with lab-based work, even though the quality of industrial data is reduced.

Industrial processes proceed through states in a non-linear fashion. Multiple different types of Idle state were identified and categorised by their context – Idle due to material change (Idle: change-over), idle due to process change (Idle: configuring), and idle due to operational events (Idle: waiting).

The Off state generally only applies when the business is not operating, and the Warm-up state only accounts for 1% of total processing time. These states can therefore be discounted from process state analysis.

The need for an Idle (waiting) state has been identified, to account for non-processing Idle time. In an R&D context, machine use is intermittent: The machine spends most of its time in the Off state, and work is carried out in individual "blocks" of time. In a continuous manufacturing environment, time used for activities such as breaks, correcting programming errors and fetching material can interfere with manufacturing and must therefore be accounted for in analysis of process states.

Comparison of Warm-up state energy and Idle state power allows decisions to be made regarding when to leave the machine idle vs. when to deactivate it. For this machine, the equivalence time is 7:41 (m:ss). This allows the Off state to be reintroduced to future analyses, as “Off (waiting)”, which acts as a counterpart to Idle (waiting) used here.

In industrial laser cutting, the bulk of emissions are generated by the Processing state (in this study, between 55 and 96%, depending on the specific process and number of sheets). This suggests that optimisation of processing parameters would provide the most effective solution for reducing emissions.

Processing parameter analysis has operational implications and is not necessarily easy, or even possible, in an SME. It is therefore vital to assess the short-term loss in productivity against the long-term gain in efficiency when making such a decision.

The Processing and Idle (change-over) states are directly proportional to each other, and both increase as the length of the process increases. The Idle (configuring) state occurs only once per process, and thus its contribution to processing emissions reduces proportionally as the process time increases. Therefore, having a smaller number of longer Processing states is more carbon-efficient than a larger number of shorter processes.

Data availability

The data underlying this article are available in the Loughborough University Repository at https://doi.org/10.17028/rd.lboro.23545758 .

Laitner, J. A. (2013). An overview of the energy efficiency potential. Environmental Innovation and Societal Transitions, 9 , 38–42. https://doi.org/10.1016/j.eist.2013.09.005

Article Google Scholar

Fortune Business Insights (2020) Industrial lasers market.

Lisco F., Goffin N., Abbas A., Claudio G., Woolley E., Tyrer J. R., & Walls J. M. (2016). Laser annealing of thin film CdTe solar cells using a 808 nm diode laser. In 2016 IEEE 43rd photovoltaic specialists conference (PVSC). IEEE, pp 2811–2816. https://doi.org/10.1109/PVSC.2016.7750165

Ford, S., & Despeisse, M. (2016). Additive manufacturing and sustainability: An exploratory study of the advantages and challenges. Journal of Cleaner Production, 137 , 1573–1587. https://doi.org/10.1016/j.jclepro.2016.04.150

Peng, T., Kellens, K., Tang, R., Chen, C., & Chen, G. (2018). Sustainability of additive manufacturing: An overview on its energy demand and environmental impact. Additive Manufacturing, 21 , 694–704. https://doi.org/10.1016/j.addma.2018.04.022

Wei, H., Zhang, Y., Tan, L., & Zhong, Z. (2015). Energy efficiency evaluation of hot-wire laser welding based on process characteristic and power consumption. Journal of Cleaner Production, 87 , 255–262. https://doi.org/10.1016/j.jclepro.2014.10.009

Goffin, N. J., Higginson, R. L., & Tyrer, J. R. (2016). Using wire shaping techniques and holographic optics to optimize deposition characteristics in wire-based laser cladding. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, 472 , 20160603. https://doi.org/10.1098/rspa.2016.0603

Wellburn, D., Shang, S., Wang, S. Y., Sun, Y. Z., Cheng, J., Liang, J., & Liu, C. S. (2014). Variable beam intensity profile shaping for layer uniformity control in laser hardening applications. International Journal of Heat and Mass Transfer, 79 , 189–200. https://doi.org/10.1016/j.ijheatmasstransfer.2014.08.020

Mok, S. H., Bi, G., Folkes, J., & Pashby, I. (2008). Deposition of Ti–6Al–4V using a high power diode laser and wire, Part I: Investigation on the process characteristics. Surface and Coatings Technology, 202 , 3933–3939. https://doi.org/10.1016/j.surfcoat.2008.02.008

Kellens, K., Rodrigues, G. C., Dewulf, W., & Duflou, J. R. (2014). Energy and resource efficiency of laser cutting processes. Physics Procedia, 56 , 854–864. https://doi.org/10.1016/j.phpro.2014.08.104

Ouyang, J., Mativenga, P. T., Liu, Z., & Li, L. (2021). Energy consumption and process characteristics of picosecond laser de-coating of cutting tools. Journal of Cleaner Production, 290 , 125815. https://doi.org/10.1016/j.jclepro.2021.125815

Baumers, M., Tuck, C., Bourell, D. L., Sreenivasan, R., & Hague, R. (2011). Sustainability of additive manufacturing: Measuring the energy consumption of the laser sintering process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225 , 2228–2239. https://doi.org/10.1177/0954405411406044

Selicati, V., Mazzarisi, M., Lovecchio, F. S., Guerra, M. G., Campanelli, S. L., & Dassisti, M. (2022). A monitoring framework based on exergetic analysis for sustainability assessment of direct laser metal deposition process. International Journal of Advanced Manufacturing Technology, 118 , 3641–3656. https://doi.org/10.1007/s00170-021-08177-x

Chiaroni, D., Chiesa, V., Franzò, S., Frattini, F., & Manfredi, L. V. (2017). Overcoming internal barriers to industrial energy efficiency through energy audit: A case study of a large manufacturing company in the home appliances industry. Clean Technologies and Environmental Policy, 19 , 1031–1046. https://doi.org/10.1007/s10098-016-1298-5

Worrell, E., Laitner, J. A., Ruth, M., & Finman, H. (2003). Productivity benefits of industrial energy efficiency measures. Energy, 28 , 1081–1098. https://doi.org/10.1016/S0360-5442(03)00091-4

Cai, W., & Hung, L. K. (2021). Sustainability assessment of mechanical manufacturing systems in the industrial sector. Renewable and Sustainable Energy Reviews, 135 , 110169. https://doi.org/10.1016/j.rser.2020.110169

Gadaleta, M., Berselli, G., & Pellicciari, M. (2017). Energy-optimal layout design of robotic work cells: Potential assessment on an industrial case study. Robotics and Computer-Integrated Manufacturing, 47 , 102–111. https://doi.org/10.1016/j.rcim.2016.10.002

Vatankhah, B. A., Liu, X., Guo, H., & Li, Z. (2021). A digital twin-driven approach towards smart manufacturing: Reduced energy consumption for a robotic cell. International Journal of Computer Integrated Manufacturing, 34 , 844–859. https://doi.org/10.1080/0951192X.2020.1775297

Le, C. V., Pang, C. K., & Gan O. P. (2012) Energy saving and monitoring technologies in manufacturing systems with industrial case studies. In Proceedings of the 2012 7th IEEE conference on industrial electronics and applications, ICIEA 2012, pp. 1916–1921. https://doi.org/10.1109/ICIEA.2012.6361042

Madan, J., Mani, M., Lee, J. H., & Lyons, K. W. (2015). Energy performance evaluation and improvement of unit-manufacturing processes: Injection molding case study. Journal of Cleaner Production, 105 , 157–170. https://doi.org/10.1016/j.jclepro.2014.09.060

Liu, W., Deng, K., Wei, H., Zhao, P., Li, J., & Zhang, Y. (2021). A decision-making model for comparing the energy demand of additive-subtractive hybrid manufacturing and conventional subtractive manufacturing based on life cycle method. Journal of Cleaner Production, 311 , 127795. https://doi.org/10.1016/j.jclepro.2021.127795

Morrow, W. R., Qi, H., Kim, I., Mazumder, J., & Skerlos, S. J. (2007). Environmental aspects of laser-based and conventional tool and die manufacturing. Journal of Cleaner Production, 15 , 932–943. https://doi.org/10.1016/j.jclepro.2005.11.030

Guarino, S., Ponticelli, G. S., & Venettacci, S. (2020). Environmental assessment of selective laser melting compared with laser cutting of 316L stainless steel: A case study for flat washers’ production. CIRP Journal of Manufacturing Science and Technology, 31 , 525–538. https://doi.org/10.1016/j.cirpj.2020.08.004

Won, J.-J., Lee, Y. J., Hur, Y.-J., Kim, S. W., & Yoon, H.-S. (2023). Modeling and assessment of power consumption for green machining strategy. International Journal of Precision Engineering and Manufacturing-Green Technology, 10 , 659–674. https://doi.org/10.1007/s40684-022-00455-7

Tesic, S., Cica, D., Borojevic, S., Sredanovic, B., Zeljkovic, M., Kramar, D., & Pusavec, F. (2022). Optimization and prediction of specific energy consumption in ball-end milling of Ti-6Al-4V alloy under MQL and cryogenic cooling/lubrication conditions. International Journal of Precision Engineering and Manufacturing-Green Technology, 9 , 1427–1437. https://doi.org/10.1007/s40684-021-00413-9

Li, C., Zhao, G., Zhao, Y., Xu, S., & Zheng, Z. (2022). Prediction model of net cutting specific energy based on energy flow in milling. International Journal of Precision Engineering and Manufacturing-Green Technology, 9 , 1285–1303. https://doi.org/10.1007/s40684-021-00397-6

Cai, W., Zhang, Y., Xie, J., Li, L., Jia, S., Hu, S., & Hu, L. (2022). Energy performance evaluation method for machining systems towards energy saving and emission reduction. International Journal of Precision Engineering and Manufacturing-Green Technology, 9 , 633–644. https://doi.org/10.1007/s40684-021-00365-0

Cai, W., Li, L., Jia, S., Liu, C., Xie, J., & Hu, L. (2020). Task-oriented energy benchmark of machining systems for energy-efficient production. International Journal of Precision Engineering and Manufacturing-Green Technology, 7 , 205–218. https://doi.org/10.1007/s40684-019-00137-x

AILU, EPSRC Centre for Innovative Manufacturing in Laser Based Production Processes. (2017). Lasers for productivity—a UK strategy.

Bjørnbet, M. M., Skaar, C., Fet, A. M., & Schulte, K. Ø. (2021). Circular economy in manufacturing companies: A review of case study literature. Journal of Cleaner Production, 294 , 126268. https://doi.org/10.1016/j.jclepro.2021.126268

Goffin, N. J., Jones, L. C. R., Tyrer, J., Ouyang, J., Mativenga, P., & Woolley, E. (2020). Characterisation of laser system power draws in materials processing. Journal of Manufacturing and Materials Processing . https://doi.org/10.3390/jmmp4020048

Goffin, N., Jones, L. C. R., Tyrer, J., Ouyang, J., Mativenga, P., & Woolley, E. (2021). Mathematical modelling for energy efficiency improvement in laser welding. Journal of Cleaner Production, 322 , 129012. https://doi.org/10.1016/j.jclepro.2021.129012

Li, W., & Kara, S. (2011). An empirical model for predicting energy consumption of manufacturing processes: A case of turning process. In Proc Inst Mech Eng B J Eng Manuf. pp. 1636–1646. https://doi.org/10.1177/2041297511398541

Kara, S., & Li, W. (2011). Unit process energy consumption models for material removal processes. CIRP Annals Manufacturing Technology, 60 , 37–40. https://doi.org/10.1016/j.cirp.2011.03.018

Gutowski, T. G., Branham, M. S., Dahmus, J. B., Jones, A. J., Thiriez, A., & Sekulic, D. P. (2009). Thermodynamic analysis of resources used in manufacturing processes. Environmental Science & Technology, 43 , 1584–1590. https://doi.org/10.1021/es8016655

Department for Business Energy & Industrial Strategy. (2021). Greenhouse gas reporting - Conversion factors 2021. https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2021 .

BSI. (2017). BS ISO 14955–1:2017 Machine tools—Environmental evaluation of machine tools. BSI

Jones, L. C. R., Goffin, N., Ouyang, J., et al. (2022). Laser specific energy consumption: How do laser systems compare to other manufacturing processes? Journal of Laser Applications, 34 , 042029. https://doi.org/10.2351/7.0000790

Ouyang, J., Mativenga, P., Liu, Z., Goffin, N., Jones, L., Woolley, E., & Li, L. (2022). Sankey diagrams for energy consumption and scope 2 carbon emissions in laser de-coating. Energy, 243 , 123069. https://doi.org/10.1016/j.energy.2021.123069

Kellens, K., Renaldi, R., Dewulf, W., Kruth, J. P., & Duflou, J. R. (2014). Environmental impact modeling of selective laser sintering processes. Rapid Prototyping Journal, 20 , 459–470. https://doi.org/10.1108/RPJ-02-2013-0018

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Acknowledgements

This work has been funded by the EPSRC, Grant number EP/S018190/1 "Research on the theory and key technology of laser processing and system optimisation for low carbon manufacturing (LASER-BEAMS)"

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Goffin, N., Jones, L.C.R., Tyrer, J.R. et al. Industrial Energy Optimisation: A Laser Cutting Case Study. Int. J. of Precis. Eng. and Manuf.-Green Tech. 11 , 765–779 (2024). https://doi.org/10.1007/s40684-023-00563-y

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Optimisation of process parameters of high power CO2 Laser cutting for advanced materials

Eltawahni, Hayat (2011) Optimisation of process parameters of high power CO2 Laser cutting for advanced materials. PhD thesis, Dublin City University.

Item Type:	Thesis (PhD)
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Uncontrolled Keywords:	Laser Cutting; optimization; DOE; RSM
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The Effect of Laser Parameters on Cutting Metallic Materials

Seungik son.

1 Department of Future Convergence Engineering, Kongju National University, Cheonan 1223-24, Korea; moc.liamg@32rlddmtsht

Dongkyoung Lee

2 Department of Mechanical and Automotive Engineering, Kongju National University, Cheonan 1223-24, Korea

This experimental study investigated the effect of laser parameters on the machining of SS41 and SUS304. The metallic materials play an important role in engineering applications. They are widely used in high-tech industries such as aerospace, automotive, and architecture. Due to the development of technology and high-tech industrialization, the various processing technologies are being developed with the requirement of high precision. However, the conventional cutting process is difficult to meet high precision processing. Therefore, to achieve high precision processing of the SS41 and SUS304, laser manufacturing has been applied. The current study investigated the process quality of laser cutting for SS41 and SUS304, with the usage of a continuous wave CO 2 laser cutting system. The experimental variables are set to the laser cutting speed, laser power, and different engineering materials. The results are significantly affected by the laser parameters. As the result, the process quality of the laser cutting has been observed by measuring the top and bottom kerf widths, as well as the size of the melting zone and Heat Affected Zone (HAZ) according to volume energy. In addition, the evaluation of the laser processing parameters is significantly important to achieve optimal cutting quality. Therefore, we observed the correlation between the laser parameters and cutting quality. These were evaluated by analysis of variance (ANOVA) and multiple regression analysis. The experimental results of kerf top, kerf bottom, melting width, and HAZ on the laser parameters are properly predicted by multiple regression. In addition, the effect of laser parameters on the materials is determinant by the percentage of contribution of ANOVA.

1. Introduction

There are various metallic materials used for production in the industrial fields. Among the metallic materials commonly used in industry, SS41 and SUS304 are the most widely used. SS41 is a structural steel containing Si and Mn. It is widely used in various fields such as aerospace, automobiles, ships, and construction due to its great mechanical properties and low cost. SUS304 is stainless steel that has high corrosion resistance due to containing Cr component. It is generally used for various applications without surface treatment because the metallic materials have low thermal deformation. It is challenging to machine SS41 and SUS304 with high precision using conventional techniques such as mechanical cutting, drilling milling. The features of the mechanical method have critical processing problems such as tools wearing [ 1 ]. However, the limitation of mechanical processing can be solved by laser processing. Thus, the laser machining using CO 2 laser is used as an alternative to the conventional method. Furthermore, the manufacturer prefers to use high-power laser processing rather than mechanical processing because the laser processing has more advantages than mechanical processing. Laser machining can be performed on various materials without tool wear and additional cost. The method is non-contact processing, which provides flexibility in processing [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Among the laser system used by industries, the CO 2 laser has more economical than other laser systems. In addition, the laser system has high stability during the cutting or drilling processes of the significantly thick materials. Even non-metallic materials can be easily processed using a CO 2 laser. The special concern for manufacturers using laser cutting is to maximize productivity with the high quality of components produced through the high-power laser cutting process. However, to improve product quality and productivity, the effects of laser parameters on the material should be considered as major issues. In addition, to control the influence of the laser beam, the laser parameters must be selected appropriately. Indeed, adjustable laser parameters include laser power, cutting speed, assist gas pressure, and stand-off distance.

To maintain high precision and good quality process, the laser parameters applied to the process should be properly selected, but the effect of the parameters is difficult to predict. Besides, many manufacturers spend a lot of time and effort to determine the laser parameter which suitable for the process. In the previous studies, experiments were carried out according to specific laser parameters, and there was a comparative analysis of the effect of each parameter on the processing quality. Lamikiz et al. [ 16 ] suggested the optimum working areas and cutting conditions for the laser cutting of steel. The main experimental parameter was the thickness of the material and the results showed a remarkable different behavior between the thinnest and the thickest sheets. Kaebernick et. al. [ 17 ] described a monitoring technique in the laser cutting. The analytical techniques proved that the surface roughness was improved by controlling laser pulses. Rajaram et. al. [ 18 ] studied the effect of parameters on the characteristics of steel specimens. The material was cut through a CO 2 laser cutting system and cutting results were analyzed with kerf width, surface roughness, and heat-affected zone. The material which was cut using the CO 2 laser showed different results depending on the change of parameters. Yilbas [ 19 ] suggested that various parameters were affected during the laser cutting process and then, the laser power and the cutting speed for the kerf width were examined. It was confirmed that the kerf width increased with the combination of the laser power and the energy coupling factor. Anghel et. al. [ 20 ] demonstrated the experiment of laser cutting on 304 stainless steel miniature gear. In the experiment, the CO 2 laser system was employed to cut the miniature. The effects of laser parameters on average surface roughness (R a ) had been investigated on the surface of craters and cracks.

The previous studies have done significant investigations on the influence of laser parameters in the laser cutting process to materials. However, there is a lack of experimental studies on comparing laser cutting of SS41 and SUS304 under different laser parameters. In this study, we studied the effect of high-power laser parameters on the different metallic materials. Multiple regression and analysis of variance (ANOVA) are used to predict the kerf width, melting width, and Heat Affected Zones (HAZ) generated after laser cutting. In addition, these are used to investigate the effects of parameter and interaction between parameters. In this paper, we firstly describe the material properties, experimental equipment, and laser parameters. Then, the experimental results are discussed. Finally, conclusions are summarized.

2. Experimental Setup and Materials

In the present study, a continuous wavelength CO 2 laser system, which has a maximum laser power of 4.4 kW (Bylaser 4400, Bystronic, Niederönz, Switzerland), was used for the cutting process. During the experiment, the stand-off distance of the laser is set to constant, and the spot diameter is fixed at 2 mm. In addition, the laser cutting process depends on assistance gases. The assistance gases, N 2 and O 2 , are common assistance gasses used for laser cutting on stainless steel or carbon steel [ 21 , 22 ]. When cutting with O 2 gas, in the case of SS41, it is easily heated up to vaporizing temperature, thus, the material is also easily cut by a laser beam. In addition, when the SUS304 is processed using N 2 gas, the oxidation can be protected during laser cutting. At the cutting process of the SS41 and SUS304, the assistance gases are used by the constant pressure of O 2 and N 2 , respectively, to maintain high processing quality. Table 1 shows the laser parameters applied to SS41 and SUS304. Different laser powers and cutting speeds were conducted to cut the materials in the experiment. The laser parameters are set in the range where the material was completely cut. Table 2 shows the chemical composition of the materials used in the experiment. In order to analyze the experimental results, the kerf widths generated after the cutting process are measured on both top and bottom surfaces [ 23 ]. In addition, melting width and Heat Affected Zone (HAZ) formed in the bottom surface of the materials are measured using an optical microscope (Dino-lite AM4013MZT4, AnMo Electronics Corporation, New Taipei, Taiwan). The schematics of the kerf widths, melting width, and HAZ are shown in Figure 1 . The kerf widths are the part where the laser is irradiated, and the material is completely cut-off. The kerf widths are measured in the kerf top and kerf bottom. The melting width is defined by the width of the materials with melting marks as in Figure 1 HAZ is the region where the microstructure of the materials has changed.

An external file that holds a picture, illustration, etc.
Object name is materials-13-04596-g001.jpg

Measurement method of SS41 and SUS304 after laser cutting ( a ) top surface ( b ) bottom surface ( c ) top surface ( d ) bottom surface.

Laser parameters.

	SS41	SUS304
Laser Power [W]	1000–3700	2100–3900
Cutting Speed [mm/s]	2000–4100	2000–3500
Assistance Gas	O	N
Gas Pressure [bar]	3	3
Thickness [mm]	2	2

Materials chemical composition.

	C	Si	Mn	P	S	Ni	Cr
SS41 Properties [%]	0.14~0.22	0.3	0.36~0.65	0.045	0.05
SUS304 Properties [%]	0.08	1.00	2.00	0.45	0.30	8.00~10.50	18.00~20.00

3. Results and Discussion

3.1. analysis of kerf width in ss41 according to volume energy.

The experimental results of laser cutting on metallic materials (SS41 and SUS304) are investigated. The kerf width of the top and bottom surface, melting width, and HAZ are analyzed according to volume energy. Volume energy is also an important parameter in the laser cutting process which is used to understand the interaction between laser and materials [ 24 ]. The volume energy ( E volume ) is a parameter that represents the irradiated laser per unit volume, and it is calculated by the laser power divided by the laser scanning speed and the laser beam size.

where P laser is the laser power [W], V s is the cutting speed [mm/min], and A is the spot area of the laser beam [mm]. Experimental results are analyzed through E volume to identify the effect on the laser powers and cutting speed.

The effect of E volume on the kerf widths of the top and bottom surface is shown in Figure 2 . The measurements of the kerf widths are conducted on both top and bottom sections of the cutting material. Each data point represents the different laser power and is obtained by averaging all measured data. The kerf widths of the top and bottom surface increase with increasing E volume . Generally, the measured kerf widths on the top surface are slightly larger than those on the bottom surface. This happens due to various reasons, such as loss of intensity of the beam, defocusing of the laser beam, or loss of gas pressure. In addition, the kerf widths of the top and bottom surface increase with increasing laser power. At the laser power of 3700 W, the kerf widths of the top and bottom surface are observed with the largest widths of 905 μ m and 675 μ m , respectively. In the interaction between laser and materials, it is evident that kerf widths are affected by E volume . As the E volume increases, the material is rapidly heated. In addition, the materials are evaporated and removed easily on the top surface. Therefore, a larger kerf width of the top surface is formed than the kerf width of the bottom surface.

An external file that holds a picture, illustration, etc.
Object name is materials-13-04596-g002a.jpg

Variation of ( a ) kerf top and ( b ) kerf bottom in SS41 according to E volume .

3.2. Analysis of Melting Width in SS41 According to Volume Energy

The effect of E volume on the melting width of the bottom surface is shown in Figure 3 . Each measured data is obtained by averaging melting width. Melting is the area where the material melts due to the laser irradiation, and melting occurs around the kerf width. At most of the laser powers applied in the experiment, melting width increases with increasing E volume . At the laser power of 3700 W, the melting width is observed with the largest width of 917 μm. The E volume is directly proportional to laser power. As the laser power increases, the thermal energy entering the materials increases so the melting width is observed with the largest value. In short, the laser beam including the laser power and cutting speed directly affect the material.

An external file that holds a picture, illustration, etc.
Object name is materials-13-04596-g003.jpg

Variation of melting width in SS41 according to E volume .

3.3. Analysis of Kerf Width in SUS304 According to Volume Energy

The effect of E volume on the kerf widths of the top and bottom surface are shown in Figure 4 . The kerf width on SUS304 is measured in the same method as SS 41. The kerf widths of the top and bottom surface also increase with increasing E volume . The kerf widths on the top surface are slightly larger than those on the bottom surface. At the laser power of 3100 W, the kerf widths of top and bottom are observed with the largest width of 796 μm and 375 μm, respectively. As mentioned, the difference between top and bottom can be caused by various factors, such as loss of intensity of the beam, defocusing of the laser beam, or loss of gas pressure for the thickness of the materials. In the case of the trend on kerf widths, kerf widths of the top and bottom surface are observed to increase with increasing E volume . The specimen is heavily influenced by the laser beam and rapidly heats up to the vaporization temperature of the material. As the laser power increases, the laser beam entering the material increases so the kerf widths of the top and bottom surface also increase.

An external file that holds a picture, illustration, etc.
Object name is materials-13-04596-g004.jpg

Variation of ( a ) kerf top and ( b ) kerf bottom in SUS304 according to E Volume .

3.4. Analysis of Heat Affected Zone in SUS304 According to Volume Energy

The effect of the E Volume on HAZ is shown in Figure 5 . HAZ is the area in which the microstructure of a material is changed by heat input. If the microstructure changes, a microcrack occurs in the processed material, it causes a partial breakdown of the product and deteriorates the quality. Therefore, it is important to reduce the HAZ during the laser cutting so that micro-cracks can be avoided. As observed from the experimental results, the effect of the E Volume on the HAZ also increases with increasing E Volume . The maximum width of HAZ is 800 μm at 3500W and the minimum width of the HAZ is 550 μm at 2100 W. This can be related to the heat input entering the material. E Volume is proportional to laser power. As the laser power increases, the heat entering materials increase and the spread of heat damage also increase. Therefore, the HAZ increases with increasing laser power.

An external file that holds a picture, illustration, etc.
Object name is materials-13-04596-g005.jpg

Variation of the Heat Affected Zone (HAZ) in SUS304 according to E Volume .

The effect of the E Volume on HAZ is shown in Figure 5 . HAZ is the area in which the microstructure of a material is changed by heat input. If the microstructure changes, a microcrack occurs in the processed material, it causes a partial breakdown of the product and deteriorates the quality. Therefore, it is important to reduce the HAZ during the laser cutting so that micro-cracks can be avoided. As observed from the experimental results, the effect of the E Volume on the HAZ also increases with increasing E Volume . The maximum width of HAZ was 800 μm at 3500 W and the minimum width of the HAZ was 550 μm at 2100 W. This can be related to the heat input entering the material. E Volume is proportional to laser power. As the laser power increases, the heat entering materials increase and the spread of heat damage also increase. Therefore, the HAZ increases with increasing laser power.

3.5. Multiple Regression

In this section, the regression analysis of laser power and cutting speed in the laser cutting process is performed. Multiple regression analysis is a mathematical model for indicating the suitability of the relationship between the independent and dependent variable [ 25 ]. In the case of the regression model, if the high order equation is used regardless of experiment data, the determination coefficient always increases. This problem is called “overfitting”. If the regression model becomes overfitting, the prediction of experimental results through the regression model becomes meaningless. Thus, the regression equation used in this study is the quadratic regression model and the equation for the regression model is followed by:

where β is the regression coefficient and can be calculated using the least-squares method, X i and X j are the independent variables of this regression equation and these are laser power and cutting speed, respectively, y is the dependent variable and represents measured data. The second-order regression model has been developed for kerf top width, kerf bottom width, melting width, and HAZ using data from the experiments. To calculate the regression coefficient β, the coefficients of the quadratic regression model are calculated. In addition, the determination coefficient ( R sq -value) and the adjusted determination coefficient ( R adj ) are calculated to check whether the data predicted by the regression model is appropriate. When the determination coefficient is close to 1, the accuracy of regression model is estimated to be suitable. The regression coefficients are determined by the t-test. The ‘SE Coef’ represents the standard error of the coefficient, and it is useful for making up a confidence interval and performing a hypothesis test. The t-test is a statistical method of the standardized value which is calculated from experimental data. The T-statistic is used to measure the magnitude of variation for the experimental data. It is calculated from experimental data to compare the null hypothesis. Each term of coefficients is tested by the null hypothesis according to the p -value. The null hypothesis is statistical proof to determine that the regression model is statistically significant. It can be determined by statistical evidence when the experimental data is meaningful. In general, a low p -value (<0.05) indicates that the predicted model can be meaningful in the experimental data. The regression coefficient suitability and coefficient of determination are shown in Table 3 and Table 4 .

The regression coefficient of SS41.

				-Value
	388.6832	80.07202	4.85417	8.75 × 10
	0.2657	0.033199	8.00299	4.35 × 10
	−0.00855	0.044628	−0.1916	0.848694
	−3 × 10	6.2 × 10	−4.87501	8.11 × 10
	−8.5 × 10	7.18 × 10	−0.1179	0.906538
	−7.6 × 10	7.02 × 10	−1.08053	0.28416
= 0.90, (adj) = 0.89
				-Value
	−5.9335	89.49774223	−0.066297523	0.947357769
	0.2503	0.037857046	6.611759502	1.0722 × 10
	0.0723	0.05112402	1.413235703	0.16266912
	−3.289 × 10	7.25866 × 10	−4.531773519	2.7836 × 10
	−1.372 × 10	8.75522 × 10	−1.566708952	0.122356105
	6.355 × 10	8.60318 × 10	0.738711082	0.462915371
= 0.89, (adj) = 0.88
				-Value
	1030.875	112.6616	9.150191	4.76 × 10
	0.2174	0.046711	4.653414	1.81 × 10
	−0.4547	0.062792	−7.24157	8.9 × 10
	−7.995 × 10	8.73 × 10	−0.91606	0.363242
	8.1839 × 10	1.01 × 10	8.104238	2.91 × 10
	−2.8654 × 10	9.88 × 10	−2.8996	0.005187
= 0.86; (adj) = 0.85

Regression coefficient of SUS304.

				-Value
	853.0468	251.3251	3.394196	0.001371
	0.167979	0.129446	1.297679	0.200474
	−0.21867	0.11114	−1.96752	0.054797
	−1.1 × 10	2.14 × 10	−0.05021	0.960161
	4.6 × 10	1.84 × 10	2.498231	0.015886
	−5 × 10	1.76 × 10	−2.82229	0.006871
= 0.80, (adj) = 0.78
				-Value
	−108.267	69.11328	−1.56651	0.123665
	0.360518	0.035597	10.12777	1.32 × 10
	−0.05506	0.030563	−1.8015	0.077778
	−5.3 × 10	5.9 × 10	−8.97845	6.35 × 10
	8.64 × 10	5.06 × 10	1.707061	0.094141
	−8.1 × 10	4.83 × 10	−1.67321	0.100659
= 0.92, (adj) = 0.91
				-Value
	2289.716	218.9419	10.4581	4.46 × 10
	−0.68795	0.112767	−6.10062	1.64 × 10
	−0.40922	0.09682	−4.22663	0.000103
	0.000132	1.87 × 10	7.041524	5.72 × 10
	6.28 × 10	1.6 × 10	3.913384	0.000281
	−1.4 × 10	1.53 × 10	−0.91761	0.363319
= 0.85, (adj) = 0.83

The results based on the regression model for kerf widths of top, bottom surface, and melting width on SS41 on the laser power and cutting speed are plotted in Figure 6 and mathematical equations are expressed in Equations (3)–(5), respectively. The regression model of kerf top is shown in Figure 6 a R sq and R sq (adj) of the kerf top are 0.90 and 0.89, respectively. When the determination coefficient is close to 1, the accuracy of the regression model is high. Therefore, the experimental data are suitable for the regression model. It also shows the most appropriate coefficient of determination among the regression models. In Figure 6 , it is found that the kerf top increases as increasing laser power. On the other hand, the variation of the kerf top is insignificant when the cutting speed increases. However, the kerf top increases when the laser power and cutting speed increase simultaneously. The regression model of kerf bottom is shown in Figure 6 b. R sq and R sq (adj) of kerf bottom are 0.89 and 0.88, respectively. This regression model is appropriate for the experimental data. It is also found that kerf bottom increase as the increasing laser power. However, the variation of kerf bottom is not variation when the cutting speed increases. It is also that the kerf top increases when the laser power and cutting speed increase simultaneously. This is similar to the experimental result of kerf top . The regression model for melting width is shown in Figure 6 c. The correlation model is suitable for experimental data. R sq and R sq (adj) were 0.86 and 0.85, respectively. This leads to the fact that the data used in the regression model were well-fitted. In the relationship of the laser parameters, the melting width increases as the increasing laser power. However, the melting width first decreases when cutting speed increases up to 3000 mm/min. After the cutting speed of 3000 m/mm, the melting width increases when the cutting speed increases. In addition, when the laser power and cutting speed increase simultaneously, the melting width increases.

An external file that holds a picture, illustration, etc.
Object name is materials-13-04596-g006.jpg

Multiple regression of SS41 ( a ) kerf top , ( b ) kerf bottom , ( c ) Melting.

The regression model for kerf widths and HAZ on SUS304 is shown in Figure 7 . The regression model of kerf top is shown in Figure 7 a and mathematical equations are expressed in Equations (6)–(8), respectively. R sq and R sq (adj) are 0.80 and 0.78, respectively. The regression model is relatively suitable for experimental data. In the relation between laser power and cutting speed, it is found that the kerf top increases as the decreasing cutting speed but the variation of the kerf top is insignificant when the laser power increase. When the laser power and cutting speed increase simultaneously the variation of kerf top is relatively low. The regression model of kerf bottom is shown in Figure 7 b. R sq and R sq (adj) of kerf bottom are 0.92 and 0.91, respectively. The experimental data are suitable for the regression model. It is also the most appropriate decision coefficient among the regression models for SUS304. In the effects of laser power and cutting speed on kerf bottom , it is also found that kerf bottom increases as the increasing laser power. However, there is a little variation of the kerf bottom when the cutting speed increases. When the laser cutting speed increases up to 35,000 mm/min and the laser power increases up to 3000 W, the kerf bottom increases but, after 3000 W laser power, then it decreases slightly. The regression model for HAZ is shown in Figure 7 c. R sq and R sq (adj) are 0.85 and 0.83, respectively. This regression is in good agreement with the experimental data. In the relationship of the laser parameters, as the cutting speed increases, HAZ decreases rapidly. In addition, HAZ first decreases when laser power increases up to 2500 W but after laser power of 2500 W the HAZ increases with increasing the laser power.

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Object name is materials-13-04596-g007.jpg

Multiple regress of SUS304 ( a ) kerf top , ( b ) kerf bottom , ( c ) Heat Affected Zone.

3.6. Analysis of Variance (ANOVA)

In this section, the effect of the laser parameter is investigated through the analysis of variance (ANOVA). The ANOVA statistically analyzes the effect of each independent variable on the dependent variable during laser cutting. The advantage of ANOVA can be identified by the important factors for each independent variable, as well as the interaction effect of each parameter on laser cutting quality [ 26 ]. The variability of the experimental data can be determined by the percentage of contribution (PCR) of each independent variable. In addition, the results of the ANOVA are represented by the 95% confidence level ( p ≤ 0.05) and it is considered that the independent variable has a statistically significant effect on the experimental data. Table 5 and Table 6 for ANOVA results show Degrees of Freedom (DF), Sum of Squares (SS), Mean squares (MS), F ratio, and percentage of contribution (PCR). The SS is the sum of the squared deviations between the mean and the variance of each experimental data. The MS represents the estimate of the population variance. This is the corresponding sum of squares divided by degrees of freedom. The F ratio is the distribution ratio obtained through a comparison of variances. It is used to test whether the variance of each group is different and whether the population mean is different. The PCR is calculated based on the estimated variance components. The higher PCR indicates that the variability of the experimental data by independent variables increases. In the results of ANOVA, the P-value on the effect of each parameter and interaction effects between parameters are less than 0.05. This indicates that the parameters used have a significant effect on the experimental results.

SS41ANOVA table.

Source	SS	DF	MS	F Ratio	-Value	PCR [%]

Laser Power	5.6 × 10	8	7.0 × 10	4.2 × 10	<0.05	59.28
Cutting Speed	1.2 × 10	7	1.7 × 10	1.0 × 10	<0.05	12.48
Laser power × Cutting speed	2.7 × 10	56	4.7 × 10	2.8 × 10	<0.05	27.99
Error	2.4 × 10	144	1.7 × 10			0.26
Total	9.5 × 10	215

Laser Power	4.0 × 10	8	5.0 × 10	1.4 × 10	<0.05	73.06
Cutting Speed	3.1 × 10	7	4.4 × 10	1.2 × 10	<0.05	5.63
Laser power × Cutting speed	1.1 × 10	56	2.0 × 10	5.6 × 10	<0.05	20.37
Error	5.1 × 10	144	3.6 × 10			0.94
Total	5.5 × 10	215

Laser Power	5.3 × 10	8	6.6 × 10	1.2 × 10	<0.05	59.65
Cutting Speed	1.1 × 10	7	1.5 × 10	2.7 × 10	<0.05	12.08
Laser power × Cutting speed	2.4 × 10	56	4.3 × 10	7.6 × 10	<0.05	27.35
Error	8.2 × 10	144	5.7 × 10			0.92
Total	8.9 × 10	215

SUS304 ANOVA table.

Source	SS	DF	MS	F Ratio	-Value	PCR [%]

Laser Power	9.0 × 10	9	1.0 × 10	1.2 × 10	<0.05	9.93
Cutting Speed	9.5 × 10	6	1.6 × 10	1.9 × 10	<0.05	10.45
Laser power × Cutting speed	7.1 × 10	54	1.3 × 10	1.6 × 10	<0.05	78.33
Error	1.2 × 10	140	8.4 × 10			1.29
Total	9.1 × 10	209

Laser Power	1.9 × 10	9	2.1 × 10	3.9 × 10	<0.05	38.03
Cutting Speed	9.9 × 10	6	1.6 × 10	3.1 × 10	<0.05	20.22
Laser power × Cutting speed	2.0 × 10	54	3.6 × 10	6.9 × 10	<0.05	40.25
Error	7.4 × 103	140	5.3 × 10			1.52
Total	4.9 × 10	209

Laser Power	6.0 × 10	9	6.7 × 10	4.3 × 10	<0.05	22.39
Cutting Speed	7.7 × 10	6	1.3 × 10	8.3 × 10	<0.05	28.74
Laser power × Cutting speed	1.1 × 10	54	2.0 × 10	1.3 × 10	<0.05	40.78
Error	2.2 × 10	140	1.6 × 10			8.09
Total	2.7 × 10	209

The ANOVA results for SS41 are shown in Table 5 . ANOVA tables demonstrate the results of laser power, cutting speed, and laser power × cutting speed for the 95% confidence level ( p < 0.05). At the ANOVA table of kerf top , it shows that the most effective variable is laser power which was 59.28% of the PCR. The other variables affecting kerf top were cutting speed and laser power × cutting speed, which were 12.48% and 27.99% of PCR, respectively. At the ANOVA table of the kerf bottom , the laser power was the most effective variable, which was 73.06% of PCR. The other variables affecting kerf bottom were cutting speed and laser power × cutting speed, which were 5.63% and 20.37% of PCR, respectively. As a result of melting width, the PCR of the laser power, cutting speed, and laser power × cutting speed were found to be 59.65%, 12.08%, and 27.35%, respectively. The ANOVA results for SUS304 are shown in Table 6 . At the ANOVA table of kerf top , it shows that the most effective variable is laser power × cutting speed which was 78.33% of the PCR. The other variables affecting kerf top were laser power and cutting speed which were 9.93% and 10.45% of PCR, respectively. As the results of kerf bottom , it shows that the most effective variable was laser power × cutting speed, which was 40.25% of PCR. The other variables affecting kerf bottom were laser power and cutting speed which were 38.3% and 20.22% of PCR, respectively. At the ANOVA results of HAZ, the PCR of the laser power, cutting speed, and laser power × cutting speed were found to be 22.39%, 28.74%, and 40.78%, respectively. In the case of SS41 analyzed by ANOVA, the most effective variable of kerf top , kerf bottom , and melting was laser power. On the other hand, at the ANOVA results of SUS304, the most effective variable of the kerf top was laser power and the most effective variables of kerf bottom and HAZ was laser power × cutting speed. The most effective variables of experimental results were different. The reason why the effective variable is different is the mechanical or chemical properties of metallic materials are different. In the case of the chemical properties of materials, SUS304 includes the chemical composition of Ni and Cr. These components improve corrosion resistance and heat resistance. Especially, The Cr component interacts with the atmosphere of the O and then, the thin film is generated on the SUS304 surface [ 27 ]. This thin film can protect from the surface corrosion and heat damage and the effect of laser power might decrease due to the protecting thin film. Therefore, we assume that the effect of laser power affecting the material is low. The complex effect of laser power × cutting speed has more influence on the material than the effect of laser power. The influence of laser parameters on the components such Ni and Cr needs further study.

4. Conclusions

Nowadays, there are many types of laser systems, such as Nd:YAG laser or CO 2 laser. The CO 2 laser system has many advantages such as providing good processing quality and high processing efficiency [ 28 ]. To achieve improvement in product quality and productivity, the effects of laser parameters on the material should be considered as a major issue. In this study, the influences of the laser parameter, such as laser power and cutting speed on the SS41 and SUS304 are studied. The experimental results of laser cutting on metallic materials are analyzed through multiple regression and analysis of variance (ANOVA). The effects of each independent variable to output variables are analyzed. The conclusions of this experiment are as follows:

We confirmed that the experimental results depend on the laser parameters. For the experimental results on E line , as E line increases, the materials are heated until they evaporate rapidly and remove material easily. Furthermore, the laser power increases, the heat entering materials increases and the spread of heat damage also increases, so the melting and HAZ width also increase.
In the case of multiple regression on the SSand SUS it is founded that the experimental results in kerf widths, melting, and HAZ are affected by laser parameters. The effect of laser power and cutting speed is analyzed through the multiple regression model. The regression equation can appropriately predict output variables from independent variables. Besides, the coefficient of determination ( R sq ) for kerf top , kerf bottom , and melting width for SSare and respectively. For the SUS the R sq for kerf top , kerf bottom , and HAZ are and respectively. Each of R sq is suitable for experimental data and the regression model makes it possible to predict the effect of laser parameters on the material.
The results of the ANOVA on the SSand SUSanalyze the effect of each independent variable on the dependent variable during laser cutting. The most effective variable in kerf top , kerf bottom , and melting width on SSis laser power. In the case of kerf top on the SUS the most effective variables are laser power × cutting speed. On the other hand, for the kerf bottom and HAZ, the interaction effects of the laser power × cutting speed have been found most effective variables. The most effective variables are determined differently on SS41 and SUS 304. This may be caused by different chemical properties of metallic materials. Especially, we assumed that the influence of Ni and Cr components in SUS304 plays a critical role in the laser cutting. Therefore, the effect of laser cutting parameters on the chemical properties of SUS304 needs further study.

Author Contributions

D.L. and S.S. conceived and designed the experiments; D.L. and S.S. performed the experiments; D.L., S.S. analyzed the data; D.L., S.S. wrote the paper. All authors have read and agreed to the published version of the manuscript.

The research described herein was sponsored by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; Ministry of Science, ICT and Future planning) (No. 2019R1A2C1089644). The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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A Generalised Approach for the Prediction of Laser Cutting Parameters

Hussain, Mohammed A (1989) A Generalised Approach for the Prediction of Laser Cutting Parameters. PhD thesis, University of Glasgow.

A design of a tasking and control environment for laser based manufacturing systems is proposed. This uses an empirical approach based on recording previous manufacturing experience in a database, in order that this can be used in planning and control of future processes. The work presented gives details of the partial implementation of this design for laser cutting systems. This makes use of Computer Aided Design and Manufacture, Computer Numerical Controlled laser cutting machines, database and computerised planning and control.

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Item Type:	Thesis (PhD)
Qualification Level:	Doctoral
Keywords:	Mechanical engineering
Date of Award:	1989
Depositing User:
Unique ID:	glathesis:1989-76840
Copyright:	Copyright of this thesis is held by the author.
Date Deposited:	14 Jan 2020 09:33
Last Modified:	14 Jan 2020 09:33
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The University of Glasgow is a registered Scottish charity: Registration Number SC004401

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Open Access

Peer-reviewed

Research Article

Laser microdissection system based on structured light modulation dual cutting mode and negative pressure adsorption collection

Roles Data curation, Formal analysis, Validation, Writing – original draft

Affiliation College of Mechatronics and Automation, Huaqiao University, Xiamen, China

Roles Formal analysis

Affiliation College of Mechanical Information Science and Engineering, Huaqiao University, Xiamen, China

Roles Writing – review & editing

* E-mail: [email protected]

Bocong Zhou,
Caihong Huang,
Dingrong Yi

Published: August 26, 2024
https://doi.org/10.1371/journal.pone.0308662
Reader Comments

Laser microdissection technology is favored by biomedical researchers for its ability to rapidly and accurately isolate target cells and tissues. However, the precision cutting capabilities of existing laser microdissection systems are hindered by limitations in overall mechanical movement accuracy, resulting in suboptimal cutting quality. Additionally, the use of current laser microdissection systems for target acquisition may lead to tissue burns and reduced acquisition rates due to inherent flaws in the capture methods. To address these challenges and achieve precise and efficient separation and capture of cellular tissues, we integrated a digital micromirror device (DMD) into the existing system optics to modulate spatial light. This allows the system to not only implement the traditional point scanning cutting method but also utilize the projection cutting method.We have successfully cut various patterns on commonly used laser microdissection materials such as PET films and mouse tissues. Under projection cutting mode, we were able to achieve precise cutting of special shapes with a diameter of 7.5 micrometers in a single pass, which improved cutting precision and efficiency. Furthermore, we employed a negative pressure adsorption method to efficiently collect target substances. This approach not only resulted in a single-pass capture rate exceeding 90% for targets of different sizes but also enabled simultaneous capture of multiple targets, overcoming the limitations of traditional single-target capture and enhancing target capture efficiency, and avoiding potential tissue damage from lasers.In summary, the integration of the digital micromirror device into laser microdissection systems significantly enhances cutting precision and efficiency, overcoming limitations of traditional systems. This advancement demonstrates the accuracy and effectiveness of laser microdissection systems in isolating and capturing biological tissues, highlighting their potential in medical applications.

Citation: Zhou B, Huang C, Yi D (2024) Laser microdissection system based on structured light modulation dual cutting mode and negative pressure adsorption collection. PLoS ONE 19(8): e0308662. https://doi.org/10.1371/journal.pone.0308662

Editor: Wenxuan Liang, University of Science and Technology of China, CHINA

Received: March 25, 2024; Accepted: July 28, 2024; Published: August 26, 2024

Copyright: © 2024 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Cells, which are the fundamental units of biological processes, are essential for biomedical research [ 1 – 3 ]. However, it is important to acknowledge that cells do not exist in isolation. The presence of different cell types in a mixture often obscures the true nature of a lesion. As a result, it is now a major challenge for biological researchers to isolate single cells from specific anatomical regions in complex heterogeneous tissues. To address this problem, scientists have conducted extensive research that has led to the development of various cell separation methods [ 4 – 6 ]. Laser microdissection is a commonly used technique for non-contact method for microdissection and isolation of biological samples under a microscope. When laser microdissection is conducted, the targeted material absorbs UV laser energy, converting it into internal heat energy. As the material’s temperature rises, it eventually reaches a critical point, leading to vaporization on the material’s surface. The vaporized material is then expelled from the processing area, creating a precise cutting opening. Laser microdissection technology enables extremely fine cutting with smooth edges, free from burrs, and does not produce carbonization. This technique ensures sample purity and high precision, making it invaluable for research in molecular biology, cytogenetics, and related fields [ 7 – 10 ]. In a study by Laura W. Harris et al. [ 11 ], laser microdissection was utilized to isolate microvascular endothelial cells and neurons from postmortem brain tissues of both schizophrenic patients and healthy individuals. The objective of their investigation was to elucidate microvascular system dysfunction in schizophrenia patients. Similarly, Eulalie Buffin et al. [ 12 ] employed laser microdissection to procure Drosophila precursor cells, aiming to study the mechanisms involved in their specification. Another study conducted by Selda Aydin et al. [ 13 ] employed laser microdissection to procure lesions, facilitating the characterization of the TP53 mutation spectrum in malignant urothelial tissues of patients with aristolochic acid nephropathy in Belgium.

The laser microdissection system utilized in the aforementioned studies relied solely on mechanical motion for point-scanning cutting. Two prevalent approaches to point scanning cutting methods in laser microdissection systems include stage movement and galvanometer systems [ 14 – 16 ]. Nevertheless, the system’s limited mechanical movement accuracy may result in incomplete trajectories or low contour quality when cutting small targets. Consequently, it is imperative to develop a laser microdissection system that is both straightforward and capable of effectively isolating complex tissues. This advancement will significantly enhance the utility of laser microdissection technology.

A fully functional laser microdissection system must accurately isolate the desired tissue and capture anatomical targets with precision. Currently, various laser capture microdissection systems are available on the market, including those offered by prominent companies such as American Thermo Fisher, German Leica, German Zeiss, and German MMI [ 17 – 19 ]. These systems employ diverse collection methods. For instance, some systems utilize adhesion capture methods whereby infrared light illuminates a thermoplastic membrane placed on cells to facilitate cell adhesion and capture [ 20 ]. However, this method exposes target cells to temperatures of up to 90 degrees Celsius [ 21 ], potentially causing thermal damage and mechanical damage during the pulling process. Additionally, this collection method may pose contamination risks. Another approach is gravity-based collection, where the cut sample falls into a collection tube under the influence of gravity [ 22 ]. This method, however, is unsuitable for cell environments requiring culture media, and there is a risk of losing target cells due to an uncontrolled falling process. Moreover, there is a collection method employing a refocused ultraviolet cutting laser that emits laser pulses, with the pressure generated by these pulses ejecting the target into a container directly above. This method, however, may potentially damage cellular DNA and RNA. Therefore, there is an urgent need for a novel and more universal method to capture target tissues.

In order to tackle the aforementioned challenges, this paper proposes a laser microdissection system that utilizes digital micromirror device (DMD) technology. In this system, the incident laser beam is shaped by the DMD to accommodate complex cutting trajectories and target tissues of varying sizes. When dealing with small targets characterized by intricate curved trajectories, the laser beam can be precisely shaped to match the contour of the area to be cut. This enables one-time cutting without the need for mechanical movement, thereby enhancing both cutting accuracy and efficiency. The point-scanning cutting method remains suitable for cutting large volume targets or tissues with simple trajectories. Moreover, the device incorporates negative pressure to ensure stable capturing of targets of different sizes and can simultaneously capture multiple targets.

2. Materials and methods

2.1 laser microdissection method based on dmd.

Fig 1 depicts the essential instruments and overall layout of the experimental platform for the laser micro-cutting system utilizing digital micromirror device (DMD) technology.

PPT PowerPoint slide
PNG larger image
TIFF original image

https://doi.org/10.1371/journal.pone.0308662.g001

The laser microdissection system detailed in this article comprises multiple components, including a UV laser, a beam expander, a digital micromirror device (DMD), a zoom lens group, and a microscope system. The laser implemented is a diode-pumped Q-switched solid-state laser produced by Spectra-Physics, specifically the EONE-349-120 model, featuring a center wavelength of 349 nm. The microscope system showcases an Olympus IX73 inverted fluorescence microscope, with technical specifications elaborated in Table 1 . The DMD device utilized is the DLP7001 from Texas Instruments, with comprehensive technical specifications outlined in Table 2 . Throughout the laser microdissection procedures, the UV laser beam is directed towards the DMD through a laser beam expander. Subsequently, the DMD shapes the incident beam and projects it based on the preloaded pattern. The laser beam undergoes zooming via a telescope system before being focused onto the tissue surface.

https://doi.org/10.1371/journal.pone.0308662.t001

https://doi.org/10.1371/journal.pone.0308662.t002

2.2 DMD laser beam shaping principle

To facilitate projection cutting, a diffractive spatial light modulator, specifically a digital micromirror device (DMD), was integrated into the system. The DMD offers several advantages over existing galvanometer systems in terms of spatial light modulation, including high-speed switching capabilities, high display resolution, precise phase control, and enhanced stability and reliability. Additionally, the DMD benefits from mass production and cost advantages. The principle of laser beam shaping in the DMD relies on controlling the state of its micromirror elements. By toggling the state of these elements, any desired projection shape can be achieved [ 23 ]. Fig 2 illustrates the fundamental process of DMD laser beam shaping. In panel (a), micromirrors are utilized to generate open "spots" with specific diameters. In panel (b), a closed-loop polygonal cutting trajectory can be attained by activating the target micromirror element while deactivating corresponding micromirror elements at other locations.

(a) Point cutting mode. (b) Nonmechanical motion cutting mode.

https://doi.org/10.1371/journal.pone.0308662.g002

The reflection produced by the DMD yields a two-dimensional pattern of spots known as diffraction orders. Depending on factors such as the pixel pitch, the tilt angle of the DMD micromirrors, the wavelength of the illumination, and the angle of incidence of the illuminating light, there can be a spectrum ranging from fully blazed to fully anti-glare conditions. A blaze condition occurs when a single diffraction order contains the majority of the energy within the entire diffraction pattern, representing the optimal scenario. Conversely, an anti-blaze condition arises when the four brightest orders in the diffraction pattern possess equal amounts of energy, a situation to be avoided in this system [ 24 – 26 ]. The diffraction diagram of the DMD is depicted in Fig 3 , while Fig 4 illustrates the energy distribution of DMD diffraction patterns in both blazed and non-blazed states. The color depth indicates the intensity of the energy within the diffraction orders.

https://doi.org/10.1371/journal.pone.0308662.g003

https://doi.org/10.1371/journal.pone.0308662.g004

The blaze angle, denoted as i , represents the angle formed between the groove surface and the grating surface. The grating constant d , signifies the distance separating two adjacent grooves. The incident angle φ indicates the angle formed between the incident light and the normal to the grating plane. Finally, the diffraction angle θ , denotes the angle between the diffracted light ray and the normal line of the grating plane.

2.3 Design of zoom lens group

After the Digital Micromirror Device (DMD) shapes the laser beam, a suitable optical path is required to scale the shaped spot and achieve a sufficiently small cutting line width, which is crucial for effectively cutting small tissue samples. To maintain the stability of the laser microdissection system, this study employs a telescope system to scale the projected pattern, as illustrated in Fig 5 .

https://doi.org/10.1371/journal.pone.0308662.g005

The narrowed laser beam width is denoted by W , the number of opened micromirrors is denoted by n , and d represents the size of the micromirror. Additionally, f 1 represents the equivalent focal length of the system components, and f 2 represents the equivalent focal length of the objective lens. The distance f 3 between the two lenses is equal to the sum of f 1 and f 2 . In this system, f 1 = 1000 mm, f 2 = 10 mm, and the magnification obtained according to Eq 1 is 100 times.

2.4 Vacuum system design

The physical setup and principles of the negative pressure adsorption system used in this study are depicted in Figs 6 and 7 .The negative pressure adsorption system comprises a suction pipe, a vacuum generator, a PLC controller, an electromagnetic proportional valve, a filtration system, and a gas source. The inner diameter of the straw employed in the experiment was 4 mm, with its end covered by a PET film layer featuring multiple holes distributed across its surface, facilitating the absorption of target tissue without penetration. The vacuum generator employed was the ZK2G07R5ALA-06 model manufactured by SMC Company. Additionally, the solenoid proportional valve and filter system, also sourced from SMC, were utilized to regulate intake pressure and filter impurities, respectively. The PLC controller used was the FX3U-16MR/ES controller manufactured by Mitsubishi Company, tasked with controlling the vacuum generator’s operational status. The air source was provided by a vacuum compressor.

(a) Working diagram of the negative pressure adsorption system (b) Physical view of the negative pressure adsorption system (c) Enlarged view of the end of the actuator suction pipe.

https://doi.org/10.1371/journal.pone.0308662.g006

https://doi.org/10.1371/journal.pone.0308662.g007

2.5 Experimental sample preparation

In this study, 6-week-old Balb/c mice were used. The experimental protocol involving animal subjects was approved by the Ethics Committee for the Management of Experimental Animals at Huaqiao University School of Medicine. To induce anesthesia, the mice were administered a mixture of ketamine (100 mg/ml) and diazepam hydrochloride (10 mg/ml) at a ratio of 10:1 via intraperitoneal injection while being housed in a well-ventilated cage. Anesthesia was confirmed by the absence of response and slow breathing. Subsequently, euthanasia was performed using cervical dislocation.

To extract brain tissue, the mouse’s head was rinsed with normal saline, and the skin was incised with scissors to expose the skull. The skull was carefully opened with a scalpel to reveal the brain tissue, which was then delicately removed using dissecting forceps. The extracted brain tissue was immediately immersed in 10% neutral buffered formalin for fixation, with a fixation duration of 24 hours at room temperature to ensure complete fixation. Following fixation, the tissue was washed three times with PBS buffer for 5 minutes each. Subsequently, the fixed brain tissue was gradually dehydrated in sequential ethanol solutions (70%, 80%, 90%, and 95% ethanol for 30 minutes each, followed by absolute ethanol for two 30-minute intervals), and then cleared in xylene twice for 1 hour each. The brain tissue was subsequently saturated by overnight immersion in molten paraffin wax, followed by embedding, sectioning into 5-micron thickness slices, and dewaxing. The sections were stained with hematoxylin for 5–10 minutes, rinsed with tap water, and then counterstained with eosin for 1–2 minutes before another rinse with tap water. Finally, the sections were sequentially rehydrated in different ethanol concentrations (70%, 80%, 90%, and 95% ethanol for 5 minutes each, followed by absolute ethanol twice for 5 minutes each) and xylene (two 5-minute soaks), before being sealed with neutral gum.

For immunohistochemical sections, mice were immobilized on an operating table, and the mammary gland area was cleansed with 70% ethanol before the mammary gland tissue was excised. The removed tissue was promptly fixed in 10% neutral buffered formalin for 24 hours at room temperature. Following fixation, the mammary gland tissue underwent gradual dehydration (30 minutes each in 70%, 80%, 90%, and 95% ethanol, followed by two 30-minute intervals in absolute ethanol), embedding, sectioning into 5-micron thickness slices, and dewaxing. Antigen retrieval was then performed in a 95°C water bath using 10 mM phosphate buffer (pH 6.0), followed by protein blocking with 5% bovine serum protein. Subsequently, primary antibodies against rabbit anti-cell markers were applied at appropriate concentrations and incubated overnight at room temperature. Following this, an appropriate amount of HRP-labeled secondary antibody was added and incubated for 1 hour at room temperature. Finally, DAB color reagent and a color-developing substrate were applied. The slices were then gradually dehydrated (1-minute incubations in 70%, 95%, and 100% ethanol), followed by sealing with hyaluronic acid ester.

3 Experimental and discussion

3.1 laser microdissection experiment.

The experimental device’s actual image is depicted in Fig 8 .

https://doi.org/10.1371/journal.pone.0308662.g008

Utilizing the DMD blazed grating model, this article utilizes a laser wavelength of 349nm and a DMD micromirror size of 13.68μm. It is apparent that at a system incident angle of 24°, the majority of energy is concentrated on the zero-order diffraction, thereby achieving maximum energy distribution. This is illustrated in Fig 9(A) , demonstrating the concentration of energy in the zero-order diffraction at the mentioned incident angle. Conversely, Fig 9(B) depicts the non-uniform distribution of laser energy across various diffraction orders at alternative angles.

(a) Distribution of laser energy when the incident angle is 24°. (b) Distribution of laser energy at other angles.

https://doi.org/10.1371/journal.pone.0308662.g009

To validate the efficacy of the dual-mode cutting improvement system, verification tests were conducted on two materials: PET film and biological tissue. The experimental findings are presented in Figs 10 and 11 . Fig 10(A) demonstrates the feasibility of the telescope system in scaling patterns on 1.4 μm thick PET film. With a loaded spot diameter of 50 microns on the DMD, the system’s L1 lens focal length f1 is 1000mm, while the 40x objective lens has an equivalent focal length f2 of 10mm, resulting in a reduction factor of 100x. In the single-factor experiment, a minimum laser current of 3A, a repetition frequency of 1KHz, and a cutting speed of 30mm/s were selected for cutting biological tissue. At these settings, the theoretical line width is calculated to be 6.84 μm, closely matching the actual line width observed in Fig 10(A) (6.94 μm), thus meeting system requirements. Fig 10(B) illustrates the projected cross-section of a five-pointed star using a laser current of 3A and a pulse frequency of 1KHz, with 300 micromirrors spaced between two vertices horizontally. Moving to Fig 11 , the results of the cutting experiment on biological tissue are shown. Employing a laser current of 3A, a repetition frequency of 1KHz, and a point scanning cutting speed of 30mm/s, the system operates on 5μm thick biological tissue. In Fig 11(A) , the DMD utilizes 100 micromirrors in point cutting mode, while in Fig 11(B) , the letters are delineated by a width of 30 micromirrors.

(a) Point scanning cutting (b) Projection cutting.

https://doi.org/10.1371/journal.pone.0308662.g010

https://doi.org/10.1371/journal.pone.0308662.g011

Figs 10 and 11 collectively validate the efficacy of the DMD-based laser microdissection method in both point scanning mode for cutting straight lines and projection cutting mode for one-time projection cutting of anisotropic patterns on PET films and biological tissues. Leveraging the DMD’s laser micro-cutting technique, precise contours can be accurately projected to the intended location, enabling swift and accurate cutting. This method surpasses traditional cutting approaches constrained by mechanical movement, allowing for enhanced cutting precision and the handling of smaller target sizes. Notably, existing laser micro-dissection systems typically feature a minimum cutting target larger than 10 microns [ 27 – 29 ].

In Fig 11(A) , noticeable dark burn marks were observed around the incision. This phenomenon primarily results from the Gaussian distribution of energy emitted by the ultraviolet laser used in this platform’s circular laser spot. The higher energy at the center of the spot directly vaporizes tissue to form the incision, whereas the lower energy distribution at the periphery of the circular spot fails to vaporize tissue completely, resulting only in surface burns and the formation of dark burn areas.

To mitigate the occurrence of dark burns from ultraviolet laser cutting in biological tissue, optimizing the energy distribution of the laser spot to achieve uniform energy across the entire circular spot can be effective in preventing localized burning. Additionally, optimizing the process parameters of laser microsurgery can help minimize the burn area as much as possible.

To further scrutinize the system’s cutting accuracy, the experiment utilized a 40x objective lens offering 100x magnification. A DMD with a side length of 13.68 μm was employed to generate a circular cutting pattern with a line width equivalent to 20 micromirror lengths, ensuring precise targeting. The cutting laser operated at a current of 3A with a pulse frequency of 1KHz. The experiment utilized 5-micron thick immunohistochemical sections of mouse breast tissue as samples. Fig 12 illustrates the cutting process alongside its corresponding outcomes.

https://doi.org/10.1371/journal.pone.0308662.g012

The experimental results showcase the capability of the DMD projection cutting mode to achieve smaller cutting sizes, with diameters measuring up to 7.5μm, surpassing those achievable with existing laser microdissection systems. This technological advancement facilitates the swift and precise isolation of minute biological tissues.

3.2 Cutting target capture experiment

To efficiently transfer adsorbent substances into the designated container, the vacuum suction of the micro pipette should not be excessively strong. As long as it meets the minimum suction requirement, it suffices to fulfill the task. The pressure characteristic curve of the vacuum generator (refer to Fig 13 ) illustrates how the vacuum level fluctuates with changes in the supply pressure. In this investigation, we identified the optimal operating conditions for the vacuum generator, setting the supply pressure at 0.45 MPa. For this experiment, the adsorption film featured a pore size of 5 micrometers. Utilizing HE-stained 5-micrometer mouse brain tissue slices as samples, each side measuring 100 micrometers square, we conducted the experiment. Fig 14(A) displays the remaining tissue portion post-target tissue capture, while Fig 14(B) exhibits the captured target tissue released onto a glass slide.

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(a) the remaining tissue portion after the target tissue is captured. (b) the target tissue released on the slide after being captured.

https://doi.org/10.1371/journal.pone.0308662.g014

To assess the capture efficiency of the negative pressure adsorption system, we conducted experiments to determine capture rates across various target volumes. Traditional targets typically range from 10 μm to 100 μm. Therefore, for our experiments, we employed sample sizes of 30 μm, 60 μm, and 90 μm. The air compressor’s supply pressure was maintained at 0.45 MPa. Utilizing HE-stained 5 μm sections of mouse brain tissue as samples, we conducted multiple tests for each target size, repeating the process 50 times. The resulting capture rates were 98%, 94%, and 92%, respectively. As the sample area increases, the contact area between the sample and the slide’s film also expands. This increased contact leads to heightened adhesion, making sample capture more challenging and consequently decreasing the capture rate.

Due to the extensive surface area of the membrane at the target collection end, multiple targets can be captured simultaneously in a single operation without necessitating multiple movements. We verified the system’s capability to capture multiple targets simultaneously. Fig 15 illustrates the tissue conditions of HE-stained sections, each with a side length of 100 μm and a thickness of 5 μm from mouse brain tissue, distributed across various areas of the capture device and simultaneously captured by the system.

https://doi.org/10.1371/journal.pone.0308662.g015

4. Conclusions

This paper introduces a laser microdissection system integrating DMD for spatial light modulation, featuring dual cutting modes and a negative pressure adsorption collection method. By controlling the flip of the DMD micromirror element during cutting, the device offers two cutting modes. The one-time projection cutting mode enables swift cutting of shaped targets smaller than 10 μm. The negative pressure adsorption system exhibits capture rates of 98%, 94%, and 92% for three sizes of mouse brain tissue targets (30 μm, 60 μm, and 90 μm), respectively. Additionally, simultaneous capture of multiple targets was successfully demonstrated. Overall, these results suggest that our newly devised dissection device enhances dissection accuracy and effectiveness. The tissues and cells collected using this system hold significant potential for various downstream applications.

Supporting information

S1 fig. specific data of the experimental platform..

https://doi.org/10.1371/journal.pone.0308662.s001

S2 Fig. Experiment on the capture rate of targets of different sizes.

Targets with a 30μm edge length captured under a 10x objective. Targets with a 60μm edge length captured under a 10x objective. Targets with a 90μm edge length captured under a 10x objective.

https://doi.org/10.1371/journal.pone.0308662.s002

S1 Table. Raw data of the capture success rate experiment.

https://doi.org/10.1371/journal.pone.0308662.s003

S2 Table. Vacuum generator exhaust characteristics.

https://doi.org/10.1371/journal.pone.0308662.s004

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17. Claudia, Bevilacqua, Bertrand, Ducos. Laser microdissection: A powerful tool for genomics at cell level. Molecular Aspects of Medicine. 2018. https://doi.org/10.1016/j.mam.2017.09.003

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1 Introduction

1.1 Energy Saving in Laser Processing

1.2 Previous Industrial Case Studies in Manufacturing Energy Saving

1.3 Investigation Objectives

2.1 Industrial System and Measurement Equipment

2.2 Energy Analysis

4 Productivity Analysis

5 Emissions Analysis

6 Discussion

6.1 Energy Consumption

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Optimisation of process parameters of high power CO2 Laser cutting for advanced materials

The Effect of Laser Parameters on Cutting Metallic Materials

Dongkyoung Lee

1. Introduction

2. Experimental Setup and Materials

3. Results and Discussion

3.2. Analysis of Melting Width in SS41 According to Volume Energy

3.3. Analysis of Kerf Width in SUS304 According to Volume Energy

3.4. Analysis of Heat Affected Zone in SUS304 According to Volume Energy

3.5. Multiple Regression

3.6. Analysis of Variance (ANOVA)

4. Conclusions

Author Contributions

Conflicts of Interest

Enlighten Theses

A Generalised Approach for the Prediction of Laser Cutting Parameters

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Laser microdissection system based on structured light modulation dual cutting mode and negative pressure adsorption collection

1. Introduction

2. Materials and methods

2.2 DMD laser beam shaping principle

2.3 Design of zoom lens group

2.4 Vacuum system design

2.5 Experimental sample preparation

3 Experimental and discussion

3.2 Cutting target capture experiment

4. Conclusions

Supporting information

S2 Fig. Experiment on the capture rate of targets of different sizes.

S1 Table. Raw data of the capture success rate experiment.

S2 Table. Vacuum generator exhaust characteristics.

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