Probabilistic-based approach using Kernel Density Estimation for gap modeling in a statistical tolerance analysis
Article dans une revue avec comité de lecture
The statistical tolerance analysis has become a key element used in the design stage to reduce the manufacturing cost, the rejection rate and to have high quality products. One of the frequently used methods is the Monte Carlo simulation, employed to compute the non-conformity rate due to its efficiency in handling the tolerance analysis of over-constrained mechanical systems. However, this simulation technique requires excessive numerical efforts. The goal of this paper is to improve this method by proposing a probabilistic model of gaps in fixed and sliding contacts and involved in the tolerance analysis of an assembly. The probabilistic model is carried out on the clearance components of the sliding and fixed contacts for their assembly feasibility considering all the imperfections on the surfaces. The kernel density estimation method is used to deal with the probabilistic model. The proposed method is applied to an over-constrained mechanical system and compared to the classical method regarding their computation time.
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Article dans une revue avec comité de lectureDANTAN, Jean-Yves; HUANG, Zhicheng; GOKA, Edoh; HOMRI, Lazhar; ETIENNE, Alain; BONNET, Nicolas; RIVETTE, Mickaël (Elsevier, 2017)Additive manufacturing (AM) became an advanced research topic due to its ability to manufacture complex shapes. But the ability to achieve predictable and repeatable shapes is critical. Therefore, to optimize the design ...
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