utilizing-fractional-factorial-experiments-in-screening-studies

Utilizing Fractional Factorial Experiments in Screening Studies

On Demand

  • 90 minutes
  • 0 Day Left
     Feb 18, 2019 - Feb 22, 2019
  •   08:00 AM - 09:30 AM ET
    05:00 AM - 06:30 AM PT

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.

In this webinar join expert speaker Steven Wachs, where he will focuses on the use of Fractional Factorial Experiments which are invaluable when a large number of factors must be investigated.

Session Highlights:

Efficient use of Design of Experiments is indispensable for activities such as:

  • 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

  • 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

Why You Should Attend:

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.

Who Should Attend:

  • Product development personnel

  • Quality personnel

  • Manufacturing personnel

  • Lab personnel

  • R&D personnel


*You may ask your Question directly to our expert during the Q&A session.

** You can buy On-Demand and view it as per your convenience.

Steven Wachs

Steven Wachs

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve 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.

Steve 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 to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.

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