Statistical Tolerance Design
I.D. # 88033 Duration 1 Day
This seminar will include a review of statistical theory and present statistical methods, which are used to better select and/or analyze Tolerance Stack-ups. The Probability (RMS) Method, the Monte Carlo Simulation Technique and tolerance optimization techniques will be discussed along with guidelines on which method(s) to use in given situations. Attendees will also view a demonstration of a microcomputer Monte Carlo Simulation program that analyzes the effects of form and assembly variation on the quality of a finished product. This seminar will provide an overview of Design of Experiments (DOE) methods, which enable effective analysis of critical product dimensions and tolerances.

Note: Participants should bring a scientific calculator for several in-class exercises.
Learning Objectives
By attending in this seminar, you will be able to:
  • Apply worst case, root-mean-square, and Monte Carlo simulation methods for the allocation of analysis of simple-to-intermediate complexity tolerancing schemes
  • Use the "Risk of Misassembly" approach for tolerance allocation, and the "Main Effect" approach for determining dimensional variables tolerance which exhibit the greatest impact on build variation
  • Understand and be exposed to various computer tools which can greatly improve their statistical tolerancing efforts, given the intricacies of GD&T, plus-minus tolerancing, and various datum schemes
Who Should Attend
This seminar is intended for engineers and educators who would like to have a good working knowledge of applying statistics to product design in order to better predict and improve product quality.
Prerequisites
An engineering undergraduate degree in any discipline would be beneficial.
Seminar Content
  • Review of Tolerancing Methods, Tolerance Stack-Ups and the Relationship between Tolerancing and Quality. A High-Level Overview of Geometric Dimensioning & Tolerancing (GD&T) and Process Capability Measurement is Provided
  • Tolerance Synthesis (Allocation) Versus Tolerance Analysis
  • Overview of the Worst Case (non-statistical) Tolerancing Method for Comparison with Statistical Tolerancing Results
  • Probability & Statistics Concepts Required for Statistical Tolerancing Methods
  • Tolerance Allocation Based on "Risk of Misassembly"
  • Statistical Tolerancing Using the Root-Mean-Square (RMS) Method
    • With bilateral tolerances
    • With unilateral and/or asymmetrical tolerances
    • In 2-D and 3-D applications
    • Participant exercises
  • Statistical Tolerancing Using Monte Carlo Simulation
    • Performing Monte Carlo Analysis by hand
    • Demonstrations of computer software for Monte Carlo Simulation and analysis (VSA-2D, VSA-3D and others, if time permits)
  • Analyzing Part Tolerances using Main Effect and Sensitivity Analysis Methods -- Methods for Determining the Contribution of Process Variables to Overall Process Variation. A Brief Description of Partial and Full-Factorial Analysis is Also Provided
  • Overview of Design of Experiments (DOE) and Tolerance Optimization Techniques -- Their Benefits in Effective Tolerancing of Parts and Assemblies
  • Tolerance Management Program Guidelines -- For the Benefit of Participants Interested in Establishing a Comprehensive Quality Assurance Program in Their Organization
Instructor(s): Kevin Zielinski
Kevin Zielinski currently owns and operates Red Cedar Media LLC, a training and corporate communications consulting, design, development and delivery company based in Michigan. Previously, Kevin was Senior Applications Specialist for EDS (including General Motors/EDS and Hewlett Packard/EDS) specializing in technical training delivery, training consulting, courseware design and development, and e-Learning. He has designed, developed and delivered over 40 lecture- and web-based courses attended by General Motors and EDS employees worldwide. Mr. Zielinski has also served as Adjunct Professor for the Wayne State University College of Engineering and WSU/Focus:Hope for many years. His areas of expertise include: e-Learning design and development, Quality Tools and Methods (Design of Six Sigma, Robust Engineering, Design of Experiments (DOE), Statistical Tolerancing and GD&T); Design for Manufacturing and Assembly (DFMA); Engineering Economics; and Plant Floor Throughput Improvement. He has been an instructor for SAE Professional Development since 1990, and is a recipient of SAE's Forest R. McFarland Award (April 2005). He holds a bachelor's and master's degree in engineering from Wayne State University.
Fees: $795 SAE Members*: $635 - $715
* The appropriate SAE Member discount will be applied through the Registration process.  Discounts vary according to level of membership: Elite Member 20%; Premium Member 15%; Classic Member 10%
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