Tolerance analysis approach based on the classification of uncertainty (aleatory / epistemic)

Jean-Yves Dantan (1) , Nicolas Gayton (2) , Ahmed Jawad Qureshi (3) , Maurice Lemaire (2) , Alain Etienne (1)

1 LCFC - Laboratoire de Conception Fabrication Commande
2 IFMA - Institut Français de Mécanique Avancée
3 Uni.lu - Université du Luxembourg

Uncertainty is ubiquitous in tolerance analysis problem. This paper deals with tolerance analysis formulation, more particularly, with the uncertainty which is necessary to take into account into the foundation of this formulation. It presents: a brief view of the uncertainty classification: Aleatory uncertainty comes from the inherent uncertain nature and phenomena, and epistemic uncertainty comes from the lack of knowledge, a formulation of the tolerance analysis problem based on this classification, its development: Aleatory uncertainty is modeled by probability distributions while epistemic uncertainty is modeled by intervals; Monte Carlo simulation is employed for probabilistic analysis while nonlinear optimization is used for interval analysis.

Mots clés: Tolerance analysis; Uncertainty; Epistemic uncertainty; Uncertainty propagation

Domaines: Analyse numérique [math.NA]; Analyse numérique [cs.NA]

Jean-Yves Dantan, Nicolas Gayton, Ahmed Jawad Qureshi, Maurice Lemaire, Alain Etienne. Tolerance analysis approach based on the classification of uncertainty (aleatory / epistemic). 12th CIRP Conference on Computer Aided Tolerancing, Apr 2012, United Kingdom. pp.287-293, ⟨10.1016/j.procir.2013.08.044⟩. ⟨hal-01018358⟩