Design of Experiments (DoE) for Engineers

EDU Trainings s.r.o.

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Module 1: Introduction

DOE example
Benefits to using the DOE process
History
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
DOEsim
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®
Pooling
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
Mixture
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)

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.

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