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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 02 Mar 2024 00:55:16 GMT2024-03-02T00:55:16ZTolerance analysis approach based on the classification of uncertainty (aleatory / epistemic)
http://hdl.handle.net/10985/8322
Tolerance analysis approach based on the classification of uncertainty (aleatory / epistemic)
GAYTON, N.; QURESHI, Ahmed Jawad; LEMAIRE, Maurice; ETIENNE, Alain; DANTAN, Jean-Yves
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.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/83222013-01-01T00:00:00ZGAYTON, N.QURESHI, Ahmed JawadLEMAIRE, MauriceETIENNE, AlainDANTAN, Jean-YvesUncertainty 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.Statistical tolerance analysis of a hyperstatic mechanism, using system reliability methods
http://hdl.handle.net/10985/9282
Statistical tolerance analysis of a hyperstatic mechanism, using system reliability methods
BEAUCAIRE, Paul; GAYTON, Nicolas; DUC, Emmanuel; LEMAIRE, Maurice; DANTAN, Jean-Yves
The quality level of a mechanism can be evaluated a posteriori after several months by following the number of warranty returns. However, it is more interesting to evaluate a predicted quality level in the design stage: this is one of the aims of statistical tolerance analysis. A possible method consists of computing the defect probability (PD) expressed in ppm. It represents the probability that a functional requirement will not be satisfied in mass production. For assembly reasons, many hyperstatic mechanisms require gaps, which their functional requirements depend on. The defect probability assessment of such mechanisms is not straightforward, and requires advanced numerical methods. This problem particularly interests the VALEO W.S. company, which experiences problems with an assembly containing gaps. This paper proposes an innovative methodology to formulate and compute the defect probability of hyperstatic mechanisms with gaps in two steps. First, a complex feasibility problem is converted into a simpler problem. Then the defect probability is efficiently computed thanks to system reliability methods and the m-dimensional multivariate normal distribution Um. Finally, a sensitivity analysis is provided to improve the original design. The whole approach is illustrated with an industrial case study, but can be adapted to other similar problems.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10985/92822012-01-01T00:00:00ZBEAUCAIRE, PaulGAYTON, NicolasDUC, EmmanuelLEMAIRE, MauriceDANTAN, Jean-YvesThe quality level of a mechanism can be evaluated a posteriori after several months by following the number of warranty returns. However, it is more interesting to evaluate a predicted quality level in the design stage: this is one of the aims of statistical tolerance analysis. A possible method consists of computing the defect probability (PD) expressed in ppm. It represents the probability that a functional requirement will not be satisfied in mass production. For assembly reasons, many hyperstatic mechanisms require gaps, which their functional requirements depend on. The defect probability assessment of such mechanisms is not straightforward, and requires advanced numerical methods. This problem particularly interests the VALEO W.S. company, which experiences problems with an assembly containing gaps. This paper proposes an innovative methodology to formulate and compute the defect probability of hyperstatic mechanisms with gaps in two steps. First, a complex feasibility problem is converted into a simpler problem. Then the defect probability is efficiently computed thanks to system reliability methods and the m-dimensional multivariate normal distribution Um. Finally, a sensitivity analysis is provided to improve the original design. The whole approach is illustrated with an industrial case study, but can be adapted to other similar problems.