Design of Experiments (DOE) for Engineers C0406

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).

Learning Objectives

By attending this course, you will 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® Software tool to analyze data

Who Should Attend

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.

Testimonials

"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

You must complete all course contact hours and successfully pass the learning assessment to obtain CEUs.