4-Hr Virtual Training: Introduction to Design of Experiments

Instructor: Steven Wachs
Product ID: 704847
  • Duration: 4 hrs
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In this training program, attendees will understand when and why to apply DOE (design of experiments). They will also learn to identify and interpret significant factor effects and 2-factor interactions and develop predictive models to explain and optimize process/product behavior. Applying efficient fractional factorial designs in screening experiments will also be discussed.

Why Should You Attend:

Participants will gain a solid understanding of important concepts and methods in statistical experiments. Successful experiments allow the development of predictive models for the optimization of product designs or manufacturing processes. Several practical examples and case studies will be presented to illustrate the application of technical concepts. This webinar will prepare attendees to begin designing and conducting experiments. Attendees will also learn how to analyze the data from experiments to understand significant effects and develop predictive models utilized to optimize process behavior.

Design of experiments has numerous applications, including:

  • Fast and Efficient Problem Solving (Root Cause Determination)
  • Shortening R&D Efforts
  • Optimizing Product Designs
  • Optimizing Manufacturing Processes
  • Developing Product or Process Specifications
  • Improving Quality and/or Reliability

Learning Objectives:

  • Understand when and why to apply DOE (design of experiments)
  • Plan and conduct experiments in an effective and efficient manner
  • Identify and interpret significant factor effects and 2-factor interactions
  • Develop predictive models to explain and optimize process/product behavior
  • Check models for validity
  • Apply very efficient fractional factorial designs in screening experiments
  • Avoid common misapplications of DOE in practice

Areas Covered in the Webinar:

Introduction to Experimental Design

  • What is DOE?
  • Definitions and Concepts
  • Sequential Experimentation
  • When to Use DOE
  • Common Pitfalls in DOE
  • Steps for Planning, Implementing and Analyzing an Experiment
  • Case Study 1

Two Level Factorial Designs

  • Design Matrix and Calculation Matrix
  • Main and Interaction Effects
  • Testing for Statistical Significance
  • Interpreting Effects
  • Using Center Points

Developing Mathematical Models

  • Developing First Order Models
  • Residuals /Model Validation
  • Solving Models
  • Optimizing Responses
  • Case Study 2

Fractional Factorial Designs (Screening)

  • Structure of the Designs
  • Confounding/Aliasing
  • Resolution
  • Analysis of Fractional Factorials
  • Other Designs

Who Will Benefit:

  • Operations/ Production Managers
  • Quality Assurance Managers
  • Process or Manufacturing Engineers or Managers
  • Product Design Engineers
  • Scientists
  • Research & Development Personnel
  • Project Managers

Instructor Profile:

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. He has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Mr. Wachs is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Mr. Wachs regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

He has an M.A. in Applied Statistics from the University of Michigan, an M.B.A, Katz Graduate School of Business from the University of Pittsburgh, 1992, and a B.S., Mechanical Engineering from the University of Michigan.

Topic Background:

Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions. Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed. Not only is this approach inefficient, it inhibits the ability to understand and model how multiple variables interact to jointly affect a response. Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation.

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