Over the past years, in order to perform virtual product validation from a geometrical point of view, variations have been simulated into CAT model. In this way it has been possible to improve the fulfilment of assembly functional requirements. In this research field, to solve 3D tolerance chains, starting from a preliminary specification of feature variation zones, the authors present a methodology, called SVA-TOL (Statistical Variation Analysis for Tolerancing), based on the TTRS model for tolerance specifications. The evaluation of 3D tolerance chains may be performed according to the worst-case and the statistical approach. The variational parameters are described by a hyper-polyhedron, whose vertices have to be calculated for worst-case analysis. Instead, Monte Carlo simulation is adopted for statistical analysis. Then, two case studies are proposed to show how the methodology works. In the first case study, a two-part assembly is analyzed with a numerical worst-case approach. In the second one, statistical results have been compared with the ones coming from the evaluations accomplished through a largely used CAT system, Vis-VSA by UGS.

A Numerical Methodology for Worst-Case and Statistical Tolerance Analysis of Rigid Part Assemblies

LANZOTTI, ANTONIO;PATALANO, STANISLAO
2008

Abstract

Over the past years, in order to perform virtual product validation from a geometrical point of view, variations have been simulated into CAT model. In this way it has been possible to improve the fulfilment of assembly functional requirements. In this research field, to solve 3D tolerance chains, starting from a preliminary specification of feature variation zones, the authors present a methodology, called SVA-TOL (Statistical Variation Analysis for Tolerancing), based on the TTRS model for tolerance specifications. The evaluation of 3D tolerance chains may be performed according to the worst-case and the statistical approach. The variational parameters are described by a hyper-polyhedron, whose vertices have to be calculated for worst-case analysis. Instead, Monte Carlo simulation is adopted for statistical analysis. Then, two case studies are proposed to show how the methodology works. In the first case study, a two-part assembly is analyzed with a numerical worst-case approach. In the second one, statistical results have been compared with the ones coming from the evaluations accomplished through a largely used CAT system, Vis-VSA by UGS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/328474
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