Design of Experiments (DOE) for Engineers Webinar
I.D. # WB0932 Duration 12 Hours
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, but are not limited to, identifying root causes to quality or production problems, identifying optimized design and process settings, achieving robust designs, and generating predictive math models that describe physical system behavior. This competency-based webinar utilizes a blend of reading, discussion and hands-on to help you learn the requirements and pre-work necessary prior to DOE execution, how to select the appropriate designed experiment to run, DOE execution, and analysis of DOE results. You will experience setting up, running, and analyzing simple-to-intermediate complexity Full Factorial and Partial Factorial experiments both by hand and using computer software. You will also set-up and analyze Robust/Taguchi and Response Surface experiments utilizing computer software.

Each participant will receive a 30 day MinitabTM product trial copy for use in the webinar. Due to the nature of the webinar format, each participant will be expected to dedicate approximately one hour to complete "homework" and/or short reading assignments in preparation for each session.

Note: A similar course is available as a classroom seminar.

Learning Objectives
By connecting with this webinar, you will be able to:
  • Determine when DOE is the correct tool to solve a given problem or issue
  • Select the appropriate DOE experiment type (DOE Goal) for a given application
  • Set up simple Full Factorial DOEs by hand, using cube plots
  • Set up and analyze any Full Factorial DOE using Minitab
  • Identify appropriate partial factorial design(s) based on one's application
  • Set-up and analyze Partial Factorial DOEs, simple Robust Design (Taguchi) DOEs, and simple Response Surface DOEs using Minitab
  • Recognize the structured process steps recommended when executing a DOE project
Who Should Attend
This webinar will benefit engineers involved in product design and/or optimization; process design and/or optimization; quality improvement efforts such as defect elimination, warranty avoidance or similar initiatives; and technicians, analysts and managers who support engineers in these efforts. This course has no specific course prerequisites. However, participants are expected to have some math background, that includes elementary statistics. Since the course includes demonstration and hands-on use of Minitab, participants should have some familiarity with Windows-based personal computer applications.
Seminar Content
Session 1
  • Introduction
  • What is DOE (with Initial Data Collection Exercise)
  • Full Factorial Experiments using Cube Plots
    • Identifying main effect and interaction terms
    • Determining effects for all terms
  • Estimating How Much Experiment Data is Enough
  • Assignment for Session 2: Review of Web-Based Demo of Minitab - Full Factorial DOE Set-up and Analysis; and Reading, Overview of DOE Statistics

Session 2
  • Set up and Analysis of a Full Factorial Experiment using Minitab
  • Minitab's DOE Results (High Level Overview of Minitab Outputs)
  • Review of Methods for Determining 'Significance'
  • ANOVA and Regression Overview
  • Assignment for Session 3: Hands-on Exercise in the use of Minitab using Simulator to Generate Data, and Reading on the Structured DOE Process

Session 3
  • Review of Exercise Assigned at the End of the Session 2
  • Review and Additional Information on DOE Statistics and Interpretation of DOE Output
  • Best Practice: The Problem Solving Process
  • Best Practice: The Structured DOE Process
  • Assignment for Session 4: Reading on Overview of Confounding and Partial Experiments

Session 4
  • The Confounding Principle and Partial Factorial Experiments
  • How Confounded Occurs in a DOE, including Identity Usage and Resolution
  • Setting up Partial Factorial Experiments using Minitab
  • Assignment for Session 5: Partial Factorial Exercise using Minitab and a Simulator to Generate Data for the DOE; Reading on Robust/Taguchi DOE

Session 5
  • Review of Exercise Assigned at the End of the Session 4
  • When Robust/Taguchi DOE is Appropriate
  • How Robust/Taguchi DOE is Different
    • Two-Step Optimization Concept
    • Control vs. Noise
    • Importance of Control-by-Noise Interactions
    • Signal-to-Noise (S/N) and Loss Statistics
  • Some Taguchi DOE Success Stories (incl. Set-up and Analysis in Minitab)
  • Demonstration of Minitab for Setting Up a Taguchi DOE
  • Assignment for Session 6: Robust/DOE Exercise using Minitab and a Simulator to Generate Data for the DOE, Reading on Overview of Response Surface Methodology

Session 6
  • Review of Exercise Assigned at the End of the Session 5
  • When Response Surface DOE is Appropriate
  • How Response Surface DOE is Different
    • Box-Behnken Concepts (with Demonstration of Minitab Set-up)
    • Central-Composite Concepts (with Demonstration of Minitab Set-up)
  • Class Exercise: Response Surface Set-up and Analysis
  • High-level Overview of Other Designs/Application: Plackett-Burman and Mixture
  • FAQ Review
  • Summary
Instructor(s): Kevin Zielinski
Kevin ZielinskiKevin Zielinski currently owns and operates Red Cedar Media LLC, a training and corporate communications consulting, design, development and delivery company based in Michigan. Previously, Kevin was Senior Applications Specialist for EDS (including General Motors/EDS and Hewlett Packard/EDS) specializing in technical training delivery, training consulting, courseware design and development, and e-Learning. He has designed, developed and delivered over 40 lecture- and web-based courses attended by General Motors and EDS employees worldwide. Mr. Zielinski has also served as Adjunct Professor for the Wayne State University College of Engineering and WSU/Focus:Hope for many years. His areas of expertise include: e-Learning design and development, Quality Tools and Methods (Design of Six Sigma, Robust Engineering, Design of Experiments (DOE), Statistical Tolerancing and GD&T); Design for Manufacturing and Assembly (DFMA); Engineering Economics; and Plant Floor Throughput Improvement. Mr. Zielinski has been an instructor for SAE Professional Development since 1990, and is a recipient of SAE's Forest R. McFarland Award (April 2005). He holds a bachelor's and master's degree in engineering from Wayne State University.

Test your PC and connection to see if it meets the minimum system requirements for the WebEx online training center before you register. Go to http://www.webex.com/lp/jointest/ and follow the onscreen instructions for "Join Meeting Test".

NOTE: The course presentation will be recorded and made available for 30 days to those who register by the deadline.

Testimonial
"The best hands-on DOE seminar. I will be using these tools in my job starting next week."
Alexis Perez
Lighting Application Engineer
Federal Mogul Corporation

"Content was professionally delivered and our objectives were easily met!"
Andrew Rogers
Process Engineer
FineLine Prototyping

Fees: $810 SAE Members*: $648 - $729
* The appropriate SAE Member discount will be applied through the Registration process.  Discounts vary according to level of membership: Elite Member 20%; Premium Member 15%; Classic Member 10%
CEU 1.2