The Ahtola Industrial Research Project - Advanced ...

Object Details


International Symposium on Robust Design (ISoRD'14) Technical University of Denmark, DTU

The Ahtola Industrial Research Project - Advanced Hybrid Method for the Tolerance Analysis of Complex Systems
Author:Antoine Dumas (LCFC, Arts et Métiers ParisTech, Campus Metz, France)
Jean-Yves Dantan (LCFC, Arts et Métiers ParisTech, Campus Metz, France)
Nicolas Gayton (Institut Francais de Mécanique Avancée, Clermont Université, Campus les Cézeaux, France)
Date: 2014-08-15     Track: Main     Session: 10:30-12:00 Poster Session : Robust Design & Reliabilty Engineering

Tolerancing decisions can profoundly impact the quality, the cost of the product and the number of scraps in mass production. Designers want tight tolerances to assure product performance; manufacturers prefer loose tolerances to reduce cost. There is a critical need for a quantitative design tool for specifying tolerances. Tolerance analysis brings the engineering design requirements and manufacturing capabilities together in a common model, where the effects of tolerance specifications on both design and manufacturing requirements can be evaluated quantitatively. Current commercial software are not able to provide a tolerance analysis of complex overconstrained mechanism without simplifying the behavior model. The aim of the AHTOLA project is to provide methods to treat industrial cases using complex numerical modeling of mechanical behavior. This project is centered on problem of complementary industrial partners (Pierburg, Valeo SE, Radiall SA) from various fields of application (automotive for Valeo SE and Pierburg, aeronautic for Radiall SA). The main scientific challenge concerns the development of hybrid approaches mixing worst case and probabilistic approaches to propagate stochastic and epistemic uncertainties for tolerance analysis (stochastic uncertainties = component variations ; epistemic uncertainties = gap configurations). The challenge is the deal between both and the probability computation in an acceptable computer time and managing the accuracy of the results.