design of experiments exercises

Design of Experiments (DoE) for Engineers PD530932

Topics: Quality, Safety & Maintenance Product development , Manufacturing processes , Design Engineering and Styling

How do you determine the root cause of a problem or identify which variable settings will make the product or process more "robust"? What if you need to gain a better understanding of a complicated system? Can you identify which variables most affect performance and obtain a well-correlated regression equation that explains how those selected system variables and their interactions affect performance?  

Design of Experiments (DOE) is an excellent, statistically based tool used to address and solve these questions in the quickest, least expensive, and most efficient means possible. It's a methodology that includes steps for identifying system variables worthy of study and the ideal experiment type to execute; for setting up an organized, efficient series of tests involving various combinations of selected variables; and for statistically analyzing the collected data to help obtain definitive answers to these problem-solving and optimization challenges. 

DOE is a methodology that includes steps for identifying system variables worthy of study and the ideal experiment type to execute; for setting up an organized, efficient series of tests involving various combinations of selected variables; and for statistically analyzing the collected data to help obtain definitive answers to these problem-solving and optimization challenges. 

This eLearning course utilizes a blend of text, videos, and hands-on activities to help you gain proficiency in executing designed experiments. It explains the pre-work required prior to DOE execution, how to select the appropriate designed experiment to run, and choosing the appropriate factors and their levels. You'll also learn how to execute the experimental tests ("runs") and analyze/interpret the results with the benefit of computer software tools, such as Minitab. 

You'll set up, run, and analyze simple-to-intermediate complexity Full Factorial, Partial Factorial, Taguchi/Robust, and Response Surface experiments both by hand and using computer software. You'll also receive an overview of Mixture experiments and information on how to install and configure a fully functional 30-day trial version of Minitab for completing practice activities and for personal evaluation. You'll gain the most value from this course by running experiments through various class exercises, with answers discussed after you've had the opportunity to execute the DOE on your own.

By participating in this on-demand course, you'll be able to:

• Determine when DOE is the correct tool to solve a given problem or issue
• Select the appropriate DOE experiment type (DOE goal) for a given application
• Set up simple Full Factorial DOEs by hand using cube plots
• Set up and analyze any Full Factorial DOE using Minitab®
• Identify appropriate Partial Factorial design(s) based on one's application
• Set up and analyze Partial Factorial DOEs, simple Robust Design (Taguchi) DOEs, and simple Response Surface DOEs using Minitab®
• Recognize the structured process steps recommended when executing a DOE project

Materials Provided

• 90 days of online  single-user  access  (from date of purchase)  to the seven and a half hour presentation
• Integrated knowledge checks to reinforce key concepts
• Online learning assessment (submit to SAE)
• Glossary of key terms
• Job aids (included in each module of published course)
• Instructions on how to access a 30-day trial of Minitab ®
• Video demonstrations of exercise solutions using Minitab ®
• Follow-up to your content questions
• 1.0 CEUs*/Certificate of Achievement (upon completion of all course content and a score of 70% or higher on the learning assessment)

*SAE International is authorized by  IACET  to offer CEUs for this course.

Is this On Demand Course for You?

This course will benefit engineers involved in problem-solving, such as product design or product formulation (e.g., fluid/material composition, prepared food recipes/preparation, etc.) and/or optimization; process design and/or optimization; quality improvement efforts, such as defect elimination, warranty avoidance or similar initiatives; test engineers who wish to maximize learning of system behavior with a minimum number of tests; and technicians, analysts, and managers who support engineers in the above efforts, so they may be effective participants in DOE activities.

Testimonial

"DOE expertise is a must have for engineers who deal with data all the time, whether it's in a simulation or test, or identifying the factors which have the most influence on the experiment." Raj Chandramohanan Sr. Project Engineer Borg Warner Inc.

"This course helped me to develop a good understanding of the DOE method and to apply it to real-world applications." Usman Asad Senior Research Associate University of Windsor

"Very insightful; it definitely helped me understand the different applications/uses of the DOE techniques." Alberto Aguilar Lead Engineer, EGR system PV&V John Deere Power Systems

For More Details

Email [email protected] , or call 1-877-606-7323 (U.S. and Canada) or 724-776-4970 (outside US and Canada).

Module 1: Introduction

• DOE example - 
• Benefits to using the DOE process - 
• Types/Goals of DOE - 
• Relationship to other tools - 
• Examples of where the DOE process was used successfully  - 

Module 2: Course Materials

• Practice assignments - 
• Reference materials - 
• Minitab® - 

Module 3: Full Factorial by Hand

• Full factorial fish review - 
• Experiment setup - 
• Cube plots - 
• Factor levels, repetitions, and “right-sizing” the experiment - 
• Basic data analysis - 
• Grand mean and main effects - 
• Interaction effects - 
• Eight-factor example - 

Module 4: Running Replicates

• Running replicates - 
• Minitab® replicate setup - 
• Replicate setup by hand - 
• One replicate in Minitab® - 
• Minitab® outputs - 
• Set up a full factorial experiment in Minitab® - 

Module 5: Statistical Analysis and Results Interpretation

• Statistics basics - 
• Significance test methods - 
• Confidence intervals - 
• ANOVA approach - 
• F-test, p-values - 
• Regression analysis - 
• Data transformations - 
• Run order restrictions - 
• Common analysis plots - 
• Practice activity - 

Module 6: Partial Factorial Experiments

• Partial factorial experiments - 
• The confounding principle - 
• Lost information and why that may not be so bad - 
• Determining combinations to run/identify usage and resolution - 
• Setting up partial factorial experiments using Minitab® - 
• Analyzing partial factorial experiment data - 

Module 7: Taguchi/Robust Experiments

• What does it mean to be "robust"? - 
• When robust/Taguchi DOE is appropriate; how robust/Taguchi DOE is different - 
• Control vs. noise factors - 
• Two-step optimization concept - 
• Loss function - 
• Importance of control-by-noise interactions - 
• Signal-to-noise (S/N) and loss statistics - 
• Classical and Taguchi DOE setup - 
• Robustness statistics - 
• Some Taguchi DOE success stories (including setup and analysis in Minitab®) - 
• Analytical and graphical output interpretation - 

Module 8: Response Surface and Other Experiments

• When response surface methodology (RSM) DOE is appropriate - 
• How response surface DOE is different - 
• Available response surface designs - 
• Cube plot setup - 
• Box-Behnken (B-B) designs (with demonstration of Minitab® setup) - 
• Central-Composite (C-C) designs (with demonstration of Minitab® setup) - 
• Analyzing RSM data - 
• D-optimal general full factorial, response surface, and mixture designs - 
• Methods for factor optimization - 
• Overview of other designs/applications: - 
• Plackett-Burman - 
• Activity: Response surface - 
• DOE Setup and analysis - 

Module 9: Best Practices

• The problem-solving process best practices - 
• Writing problem and objective statements - 
• Ensuring DOE is the correct tool - 
• The structured DOE process best practices - 
• Selecting response variables and experiment factors - 
• Actual versus surrogate responses - 
• Experiment logistics - 
• Test setup and data collection planning - 
• Selecting and evaluating a gage (for physical experiments) -

"There are no specific course prerequisites; however, participants are expected to have some math background, including the ability to calculate elementary statistics parameters, such as an average and a range. Since the course includes demonstration and hands-on use of Minitab®, participants should have some familiarity with Windows-based personal computer applications.