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Welcome to Design of Experiments (DOE)

DOEExperimental methods are widely used in research as well as in industrial settings, however, sometimes for very different purposes. The primary goal in scientific research is usually to show the statistical significance of an effect that a particular factor exerts on the dependent variable of interest.

In industrial settings, the primary goal is usually to extract the maximum amount of unbiased information regarding the factors affecting a production process from as few (costly) observations as possible. While in the former application (in science) analysis of variance (ANOVA) techniques are used to uncover the interactive nature of reality, as manifested in higher-order interactions of factors, in industrial settings interaction effects are often regarded as a "nuisance" (they are often of no interest; they only complicate the process of identifying important factors).

 
Endorsed Presentations:
DOE presentation
DOE Presentation Chartitnow
DOE

Design of Experiments (DOE) PowerPoint Training Presentation
This professionally-developed presentation is designed as a 40-60 minute overview of the topic. Many customers use the presentation as part of a group learning event, individual training at a computer, or as foundation slides for a more detailed or customized training presentation.

The presentation is delivered in native Microsoft PowerPoint format for easy customization. All slides may be changed to accommodate your desired use. The only restriction is that the presentation cannot be resold without permission of Superfactory.
  • Number of slides: 23
Table of Contents:
  • Introduction
  • What is Design of Experiments?
  • Method Step 1: Model Variables
  • Step 2: Set Variable Targets
  • Step 3: Experimental Plan
  • Step 4: Testing
  • Step 4: Analysis
  • Effects, Replicates & Interactions

Design Of Experiments

Design Of Experiment (DOE), is a statistical experimentation approach that enables understanding of how the variables (Factors) in a process contribute and interact to affect the output (Response) of that process. The ChartitNOW package contains the following:
  • Design of Experiment Instructions
  • Three Factor DOE Template
  • Orthogonal Arrays Illustrations

DOE: Screening Experiments Course Outline -- 3 Units

Unit 1 Background for DOE Lesson 1: Why DOE? · Limitations of OATs (one-at-a-time) experimentation. · How designed experiments overcome the limitations of OATs and are a more effective and efficient way to characterize and improve processes and products. Lesson 2: DOE Terminology · An explanation of the key terms used in designed experiments. Lesson 3: Types of Designed Experiments · Full Factorials. · Fractional Factorials. · Screening Experiments. · Response Surface Analysis. · EVOP. · Mixture Experiments. Lesson 4: Tests of Significance · Alpha and Beta Risks. · Degrees of Freedom. · Hypothesis Tests. · t-Tests. · F-Tests. Lesson 5: Setting Up a Designed Experiment · Design & Communicate the Objective. · Define the Process. · Select a Response and Measurement System. · Select Factors to be Studied. · Select the Experimental Design. · Set Factor Levels. · Final Design Considerations. Challenge: An assessment of the learner's progress in this unit.

Unit 2 - Plackett-Burman Experiments Lesson 1: Plackett-Burman Matrices · The derivation of Plackett-Burman designs. · Types of Plackett-Burman matrices. · Ways to determine the experimental error. · Techniques for analyzing experimental results. Lesson 2: Calculating Statistical Significance · Multiple techniques for testing the statistical significance of factor effects. · Using graphical techniques to analyze responses and interactions. Lesson 3: Calculating a Prediction Equation · Developing a prediction equation using factor effects. · Using the prediction equation to optimize the process or product. Lesson 4: Analyzing for the Effect on Variation · How to analyze variation as a response. · Creating a scree diagram to graphically analyze factor effects on variation. Lesson 5: When Bad Things Happen to Good Experiments · The need for good planning to prevent problems. · Some techniques for salvaging an experiment if data are lost or suspect. Challenge: An assessment of the learner's progress in this unit.

Unit 3 - Taguchi Techniques Lesson 1: Taguchi Concepts · The concept of robustness. · The Taguchi Loss Function. · Signal to noise ratios. Lesson 2: Taguchi Matrices · Taguchi designs for two-level experiments. · Use of Taguchi Interaction Tables. Lesson 3: Taguchi Experimental Analysis · Multiple techniques for testing the statistical significance of factor effects. · Using graphical techniques to analyze responses and interactions. Lesson 4: Determining Where to Set Factors · Developing a prediction equation. · Use the mean, signal to noise ratio, and variation effects to determine where to set factors. Lesson 5: When Bad Things Happen to Good Experiments · The need for good planning to prevent problems. · Some techniques for salvaging an experiment if data are lost or suspect. Challenge: An assessment of the learner's progress in this unit.


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