Course structure
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.
