ENGINEERING TOOLS & METHODS
wish to maximize learning of system behavior with a minimum number of tests; and technicians, analysts and managers who support engineers in the above efforts, so they may be effective participants in DOE activities. This course has no specific course prerequisites. However, participants are expected to have some math background, including the ability to calculate elementary statistics parameters such as an average and a range. Since the course includes demonstration and hands-on use of Minitab, participants should have some familiarity with Windows-based personal computer applications. Topical Outline SESSION ONE • 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: Hands-on Exercise in the use of Minitab using Simulator to Generate Data SESSION TWO • Review of Exercise Assigned at the end of Session 1 • Set up and Analysis of a Full Factorial Experiment using Minitab • Review of Minitab’s DOE Results • Review of Methods for Determining Significance • ANOVA and Regression Overview • Assignment for Session 3: Hands-on Exercise using Minitab to Analyze Data and Interpreting Statistical and Graphical DOE Results SESSION THREE • Review of Exercise Assigned at the end of the Session 2 • The Confounding Principle • The Benefits and Disbenefits of Confounding and of Partial Factorial Experiments • How Confounding Occurs in a DOE, including Generators and “Design Resolution” Importance of the “Alias String” • Minitab Demonstration: Setting up Partial Factorial Experiments using Default Generators and by Specifying Generators • Assignment for Session 4: Partial Factorial Exercise using Minitab and a Simulator to Generate Data for the DOE SESSION FOUR • Review of Exercise Assigned at the end of the Session 3 • 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 −− Studying Robustness with Classical DOE vs. Taguchi −− Taguchi’s Robustness Statistics: Signal-to-Noise (S/N) and Loss −− Applications of Taguchi DOE (incl. Set-up and Analysis in Minitab)
Design of Experiments (DOE) for Engineers 12 Hours | Web Seminar or On Demand Course I.D.# WB0932 or 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. Note: A similar course is available as a classroom seminar (ID# C0406) Learning Objectives By participating in this web seminar, 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 • Select the 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 • Identify and execute the structured process steps recommended when executing a DOE project Who Should Attend This course will benefit: engineers involved in problem-solving such as product design or product formulation (e.g., fluid/ material composition, prepared food recipes/preparation, etc.) and/or optimization; process design and/or optimization; quality improvement efforts such as defect elimination, warranty avoidance or similar initiatives; test engineers who
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3 ways to get a no-obligation price quote to deliver a course to your company: Call SAE Corporate Learning at +1.724.772.8529 | Fill out the online quote request at sae.org/corplearning | Email us at corplearn@sae.org
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