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Modeling and Optimizing Process Behavior using Design of Experiments - Webinar

  • ID: 4179530
  • Webinar
  • May 2017
  • Region: Global
  • 75 Minutes
  • NetZealous LLC
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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. Many common types of experiments and their applications are presented. These include experiments appropriate for screening, optimization, mixtures/formulations, etc. 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.

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

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.

Why Should You Attend?

Learn a methodology to perform experiments in an optimal fashion
Review the common types of experimental designs and important techniques
Develop predictive models to describe the effects that variables have on one or more responses
Utilize predictive models to develop optimal solutions

Areas Covered in the Session:

Motivation for Structured Experimentation(DOE)
DOE Approach / Methodology
Types of Experimental Designs and their Applications
DOE Techniques
Demonstrating Reliability with zero or few failures
Developing Predictive Models
Using Models to Develop Optimal Solutions
Case Study

Learning Objectives:

Understand where and how DOE should be used
Be able to make immediate improvements in using experimentation for problem solving, product development, process improvement, etc.
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  • Steven Wachs Steven Wachs,
    Principal Statistician ,
    Integral Concepts, Inc


    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 estimate and reduce warranty.

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