Toward template-based tolerancing from a Bayesian viewpoint

A novel approach to part tolerancing with measurement error based on Bayesian methods is presented. Parts are represented by parameterised constraint templates. A priori knowledge about expected part geometry is introduced through a prior distribution and template parameter distributions (rather than just nominal parameter values) estimated from data sets using Gibbs sampling. The case of a toleranced dimension and linear constraints is analyzed. An extension to nonlinear constraints is briefly described.<<ETX>>

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