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Design of Experiments (DOE) for Engineers Web Seminar RePlay PD330932ON

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 web seminar 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 course. Due to the nature of the online format, each participant will be expected to dedicate approximately one hour to complete "homework" and/or short reading assignments in preparation for each session.

Objectives

By participating in this course, 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

Materials Provided

  • 90 days of online single-user access (from date of purchase) to the to the six session,
    approximately twelve hour, recorded presentation
  • Course workbook (downloadable, .pdf's)
  • Online learning assessment
  • Instructor follow up to your content questions
  • 1.2 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.

Is this Web Seminar RePlay for You?

This course 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.

 

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Related Topics
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

  • Windows 7, 8, 10 (other operating systems and mobile platforms are not supported but may work)
  • Internet Explorer 11, Mozilla Firefox 37+ , Google Chrome 42+ (other browsers are not supported)
  • Broadband-1Mbps minimum

Joe J. Doe
Kevin M. Zielinski

Kevin 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. He 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.

1.2 CEUs

Testimonial

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Process Engineer
FineLine Prototyping

 

Access Period:90 Days

Duration: 12 Hours
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