Design of Experiments (DOE) 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 course utilizes hands-on activities to help you 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. You 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. You will also receive an overview of Robust DOE, including the Taguchi DOE Method.

Participants will be given information on how to receive, install and configure a fully- functional 30-day trial version of MiniTab® for their use in class, and/or for their personal evaluation. Participants are required to bring a laptop computer and/or a calculator to the course.

Note: Similar courses available as eLearning! Design of Experiments (DOE) for Engineers (PD530932) or Introduction to Design of Experiments (DOE) for Engineers (PD530932ON).

• 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® Software tool to analyze data

This course will benefit engineers, designers and quality professionals in research, design, development, testing and manufacturing who are interested or active in one or more of the applications listed above. Individuals should have an engineering degree or equivalent coursework in math, statistics and computers.

• Icebreaker: Team Problem Solving Exercise Using Engineering Judgment
• What is DOE?
  • 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 -- Statistics Basics; Significance Test Methods; Effect of Non-Random Experiments; Estimating Significance Test 'Power'; Confidence Intervals; Estimating Random Error
  • Analysis Plots -- Normal and Half-Normal Plots; Main Effect and Interaction Plots
  • Regression Analysis of Simple Full Factorial Experiments
  • Using MiniTab® for Full Factorial DOE Experiments
• Fractional (Partial) Factorial Experiments
  • The Confounding Principle -- How it Works; What Information We Lose with Confounding (and why we might not care!)
  • Selecting and Using Generators (Identities) to Set Up Confounding Strings
  • Determining Which Factor Combinations to Run
  • Analyzing Fractional Factorial Experiment Data
  • Using MiniTab® for Fractional Factorial Experiments
• Robust Design Experiments (Overview)
  • 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.) -- Ideal Situation(s) to Use Each Response Surface DOE Type; Cube Plot Set-up of Each Response Surface DOE
  • Analyzing Response Surface Experiment Data
  • Methods for Finding Optimum Factor Values
  • Using MiniTab® for response Surface Experiments
• Miscellaneous Notes and Wrap-up

"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.

"Good course - I recommend every engineer attend this course!!"
Irfan Bhatti
Technical Specialist NVH
Wolverine

"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

"Time well spent away from the office. I recommend this course to all recent college graduates in engineering."
Eric Andrews
Associate Engineer
American Axle Manufacturing

"Kevin has a very strong grasp on this subject matter and his examples and exercises really enhance the learning process."
Mufaddel Dahodwala
Project Engineer
FEV Inc

"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