4-Hr Virtual Training: Introduction to Measurement Systems Assessment

Instructor: Steven Wachs
Product ID: 704839
  • Duration: 4 hrs
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This training program will offer an introduction to measurement systems, and cover implementation of Gage R&R studies, linearity assessment, attribute measurement systems, and non-replicable systems.

Why Should You Attend:

Important measurement system characteristics include discrimination, accuracy, precision (repeatability and reproducibility), linearity, and stability. Techniques exist to assess measurement systems for each of these important characteristics. Skipping such assessments can lead to the use of measurement systems that are not capable of monitoring process variation or, in extreme cases, even of distinguishing between conforming and non-conforming product. In short, validating measurement systems is an important pre-requisite to relying on data.

This webinar will explore why measurement systems must be properly assessed to minimize risk and comply with customer and regulatory requirements.

Learning Objectives:

  • Understand key sources of measurement error
  • Design and conduct Gage R&R studies to estimate measurement error components (repeatability, reproducibility)
  • Interpret Gage R&R results and identify corrective actions, if necessary
  • Plan and conduct Gage R&R studies for attribute (pass/fail) measurement systems
  • Apply control charts to monitor measurement systems stability over time
  • Assess accuracy and linearity of measurement systems
  • Handle non-replicable systems (such as destructive tests)

Areas Covered in the Webinar:

Introduction to Measurement Systems

  • Definitions
  • Measurement Processes
  • Discrimination
  • Accuracy, Precision, Linearity
  • Repeatability and Reproducibility
  • Product, Measurement, and Total Variation

Gage R&R

  • Planning Measurement Assessments
  • Implementation of Gage R&R Studies
  • Analysis of Gage R&R Studies

Linearity Assessment

  • Linearity and Bias Studies
  • Linearity with Respect to Accuracy
  • Linearity with Respect to Precision

Attribute Measurement Systems

  • Planning for Attribute Systems Assessments
  • Short Assessment Method

Non-Replicable Systems (Destructive Testing)

Who Will Benefit:

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

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:

The effective use of data to drive decision making requires adequate measurement systems. When interpreting data or the results of data analysis, we assume that data or results represent the process. However, excessive measurement error may result in inappropriate conclusions. Thus, it is critical to properly assess whether measurement systems are adequate for their intended use prior to their use. Only capable measurement systems should be utilized to support quantitative methods such as statistical process control, inspection activities, process capability assessment, hypothesis testing, data modeling, etc.

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