Design of Experiments Training
DOE Design of Experiments
Design of Experiments (DOE), also known as Statistical Experimental Planning, is a key Six Sigma tool. It is a methodology that can be effective for general problem-solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include identifying proper design dimensions and tolerances, achieving robust designs, generating predictive math models that describe physical system behavior, and determining ideal manufacturing settings.
This competency-based course utilizes hands-on activities to help participants learn the criteria for running a DOE, the requirements and pre-work necessary prior to DOE execution, and how to select the appropriate designed experiment type to run. Participants will experience setting up, running, and analyzing the results of simple-to-intermediate complexity, Full Factorial, Partial Factorial, and Response Surface experiments utilizing manual methods as well as a hands-on computer tool that facilitates experimental design and data analysis. Participants will also receive an overview of Robust DOE, including the Taguchi DOE Method. The course will also include the use of the Minitab / JMP software tool for analyzing data.
Duration: 4 Days
Quality Managers, Quality Engineers, Manufacturing Engineers, Production Engineers, Project Engineers and Design Engineers.
Softwares for DOE Training:
The following software will be used for the training ( choose 1 only):
Minitab DOE Training
JMP DOE Training
1. JMP is a suite of computer programs for statistical analysis developed by the JMP business unit of SAS Institute.
2. Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner.
This course will enable participants to be able to:
- Decide whether to run a DOE to solve a problem or optimize a system
- Set-Up a Full Factorial DOE Test Matrix, in both Randomized and Blocked forms
- Analyze and Interpret Full Factorial DOE Results using ANOVA, (when relevant) Regression, and Graphical methods
- Set-Up a Fractional (Partial) Factorial DOE, using the Confounding Principle
- Analyze and Interpret the results of a Fractional Factorial DOE
- Recognize the main principles and benefits of Robust Design DOE
- Decide when a Response Surface DOE should be run
- Select the appropriate Response Surface Design (either Plackett-Burman, Box-Behnken, Central Composite, or D-Optimal)
- Interpret Response Surface Outputs
- Utilize the Minitab / JMP Software tool to analyze data
What is DOE?
- Brief History
- Types of Designed Experiments
- Application Examples
- Where DOE Fits in with Other Tools/Methods
DOE Requirements: Before You Can Run an Experiment
- Writing Problem and Objective Statements
- Ensuring DOE is the Correct Tool
- Selecting Response Variable(s) and Experimental Factors
- Actual vs. Surrogate Responses
- Attention to Experiment Logistics
- Test Set-up and Data Collection Planning
- Selecting and Evaluating a Gage
Full Factorial Experiments
- Introduction to Cube Plots for 3- or 4-factor 2-level Experiments
- Experiment Set-Up
- Factor Levels, Repetitions, and “Right-Sizing” the Experiment
- Experiment Terms to Estimate (Main Effects and Interactions)
- High-Level Significance Evaluation
DOE Statistical Analysis
- ANOVA Principles for Simple Full Factorial Experiments
- Analysis Plots
- Regression Analysis of Simple Full Factorial Experiments
- Using Minitab / JMP for Full Factorial DOE Experiments
Fractional (Partial) Factorial Experiments
- The Confounding Principle
- Selecting and Using Generators (Identities) to Set Up Confounding Strings
- Determining Which Factor Combinations to Run
- Analyzing Fractional Factorial Experiment Data
- Using Minitab / JMP for Fractional Factorial Experiments
Robust Design Experiments
- What is Robustness?
- Control and Noise Factors
- Classical and Taguchi Robust DOE Set-Up
- Robustness Metrics
- Analytical and Graphical Output Interpretation
Response Surface Modeling
- What Response Surface Models do BEST
- Available Response Surface DOEs (Plackett-Burman, Box-Behnken, etc.)
- Analyzing Response Surface Experiment Data
- Methods for Finding Optimum Factor Values
- Using Minitab / JMP for response Surface Experiments
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