Design for Six Sigma Statistics, Chapter 11 - Predicting the Variation Caused by Tolerances
Here is a chapter from Design for Six Sigma Statistics, written by a Six Sigma practitioner with more than two decades of DFSS experience who provides a detailed, goal-focused roadmap. It shows you how to execute advanced mathematical procedures specifically aimed at implementing, fine-tuning, or maximizing DFSS projects to yield optimal results. For virtually every instance and situation, you are shown how to select and use appropriate mathematical methods to meet the challenges of today's engineering design for quality.
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112 Implementing Consistent Tolerance Design
113 Predicting the Effects of Tolerances in Linear Systems
114 Predicting the Effects of Tolerances in Nonlinear Systems
115 Predicting Variation with Dependent Components
116 Predicting Variation with Geometric Dimensioning and Tolerancing
117 Optimizing System Variation
analyzed applied Bezel Bill box transfer function calculate capability metrics cell characteristics Click OK coefficient Constraints correlation Crystal Ball toolbar CTQs Debbie decision variables defect rate Define Forecast DFSS project engineers estimate example Excel worksheet function Y f(X GD&T histogram Hole 1 loc illustrates Johnny Karen laminations Latin hypercube sampling linear transfer functions lower tolerance limit LTILTO mean menu MINITAB Monte Carlo analysis nominal values normal distribution number of trials Option OptQuest overlay chart Pareto chart perform precision control predict random numbers random variables represents requirements resistor Root-Sum-Square RSS method Run Preferences form Screw Diameter sensitivity chart Six Sigma specify spreadsheet spring force standard deviation stochastic optimization Table target value tolerance design tolerance zone uniform distribution upper tolerance limit VTrip-Down VTrip-Up weighting factor worst-case limits WRSS X1 and X2 YNom YVar