Fractional Factorial Experiments for Screening Studies

This webinar will review the key concepts behind Design of Experiments, and the use of DOE for Process and Product Optimization, In this webinar attendees will learn the key concepts behind Design of Experiments (DOE), The webinar will emphasis fractional factorial studies which are useful in the screening phase of experimentation.

Steven Wachs
Instructor:
Steven Wachs
Date:
Thursday, April 25, 2019
Time:
10:00 AM PDT | 01:00 PM EDT
Duration:
75 Minutes

More Trainings by this Expert   Product Id : 502462

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$190 Recorded
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Overview:

In this webinar attendees will learn the key concepts behind Design of Experiments (DOE) and the use of DOE for Process and Product Optimization.

Also it will highlight the fractional factorial studies which are useful in the screening phase of experimentation.

Why should you Attend: 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. This webinar focuses on the use of Fractional Factorial Experiments which are invaluable when a large number of factors must be investigated.

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

This webinar will review the key concepts behind Design of Experiments. A strategy for utilizing sequential experiments to most efficiently understand and model a process is presented.

The webinar will emphasis fractional factorial studies which are useful in the screening phase of experimentation.

Several important techniques in experimental design (such as replication, blocking, and randomization) are introduced. A Case Study involving optimizing a manufacturing process with multiple responses is presented.

Areas Covered in the Session:
  • Motivation for Structured Experimentation (DOE)
  • DOE Approach / Methodology
  • Fractional Factorial Experimental Designs
  • Design Resolution and Choosing an Appropriate Fraction
  • Other DOE Techniques
  • Developing Predictive Models
  • Case Study

Learning Objectives:
  • Learn a methodology to perform experiments in an optimal fashion
  • Utilize very efficient fractional factorial designs to minimize the size of an experiment without sacrificing the ability to estimate important effects
  • Develop predictive models to describe the effects that variables have on one or more responses
  • Utilize predictive models to develop optimal solutions

Who Will Benefit:
  • Product Development Personnel
  • Quality Personnel
  • Manufacturing Personnel
  • Lab Personnel
  • R&D Personnel


Speaker 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.




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