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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Fri, 23 Feb 2024 00:44:22 GMT2024-02-23T00:44:22ZA statistical tolerance analysis approach for over-constrained mechanism based on optimization and Monte Carlo simulation
http://hdl.handle.net/10985/9296
A statistical tolerance analysis approach for over-constrained mechanism based on optimization and Monte Carlo simulation
QURESHI, Ahmed Jawad; SABRI, Vahid; BEAUCAIRE, Paul; GAYTON, Nicolas; DANTAN, Jean-Yves
Tolerancing decisions can profoundly impact the quality and cost of the mechanism. To evaluate the impact of tolerance on mechanism quality, designers need to simulate the influences of tolerances with respect to the functional requirements. This paper proposes a mathematical formulation of tolerance analysis which integrates the notion of quantifier: ‘‘For all acceptable deviations (deviations which are inside tolerances), there exists a gap configuration such as the assembly requirements and the behavior constraints are verified’’ & ‘‘For all acceptable deviations (deviations which are inside tolerances), and for all admissible gap configurations, the assembly and functional requirements and the behavior constraints are verified’’. The quantifiers provide a univocal expression of the condition corresponding to a geometrical product requirement. This opens a wide area for research in tolerance analysis. To solve the mechanical problem, an approach based on optimization is proposed. Monte Carlo simulation is implemented for the statistical analysis. The proposed approach is tested on an over-constrained mechanism.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10985/92962012-01-01T00:00:00ZQURESHI, Ahmed JawadSABRI, VahidBEAUCAIRE, PaulGAYTON, NicolasDANTAN, Jean-YvesTolerancing decisions can profoundly impact the quality and cost of the mechanism. To evaluate the impact of tolerance on mechanism quality, designers need to simulate the influences of tolerances with respect to the functional requirements. This paper proposes a mathematical formulation of tolerance analysis which integrates the notion of quantifier: ‘‘For all acceptable deviations (deviations which are inside tolerances), there exists a gap configuration such as the assembly requirements and the behavior constraints are verified’’ & ‘‘For all acceptable deviations (deviations which are inside tolerances), and for all admissible gap configurations, the assembly and functional requirements and the behavior constraints are verified’’. The quantifiers provide a univocal expression of the condition corresponding to a geometrical product requirement. This opens a wide area for research in tolerance analysis. To solve the mechanical problem, an approach based on optimization is proposed. Monte Carlo simulation is implemented for the statistical analysis. The proposed approach is tested on an over-constrained mechanism.Statistical tolerance analysis of over-constrained mechanisms with gaps using system reliability methods
http://hdl.handle.net/10985/9295
Statistical tolerance analysis of over-constrained mechanisms with gaps using system reliability methods
BEAUCAIRE, Paul; GAYTON, Nicolas; DUC, Emmanuel; DANTAN, Jean-Yves
One of the aims of statistical tolerance analysis is to evaluate a predicted quality level at the design stage. One method consists of computing the defect probability PD expressed in parts per million (ppm). It represents the probability that a functional requirement will not be satisfied in mass production. This paper focuses on the statistical tolerance analysis of over-constrained mechanisms containing gaps. In this case, the values of the functional characteristics depend on the gap situations and are not explicitly formulated with respect to part deviations. To compute PD, an innovative methodology using system reliability methods is presented. This new approach is compared with an existing one based on an optimization algorithm and Monte Carlo simulations. The whole approach is illustrated using two industrial mechanisms: one inspired by a producer of coaxial connectors and one prismatic pair. Its major advantage is to considerably reduce computation time.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/92952013-01-01T00:00:00ZBEAUCAIRE, PaulGAYTON, NicolasDUC, EmmanuelDANTAN, Jean-YvesOne of the aims of statistical tolerance analysis is to evaluate a predicted quality level at the design stage. One method consists of computing the defect probability PD expressed in parts per million (ppm). It represents the probability that a functional requirement will not be satisfied in mass production. This paper focuses on the statistical tolerance analysis of over-constrained mechanisms containing gaps. In this case, the values of the functional characteristics depend on the gap situations and are not explicitly formulated with respect to part deviations. To compute PD, an innovative methodology using system reliability methods is presented. This new approach is compared with an existing one based on an optimization algorithm and Monte Carlo simulations. The whole approach is illustrated using two industrial mechanisms: one inspired by a producer of coaxial connectors and one prismatic pair. Its major advantage is to considerably reduce computation time.Statistical tolerance analysis of a mechanism with gaps based on system reliability methods
http://hdl.handle.net/10985/9297
Statistical tolerance analysis of a mechanism with gaps based on system reliability methods
BEAUCAIRE, Paul; GAYTON, Nicolas; DUC, Emmanuel; DANTAN, Jean-Yves
One of the aim of statistical tolerance analysis is to evaluate a predicted quality level in the design stage. A method consists in computing the defect probability D P expressed in parts per million (ppm). It represents the probability that a functional requirement will not be satisfied in mass production. This paper focuses on the statistical tolerance analysis of over-constrained mechanism with gaps. In this case, the values of the functional characteristics depend on the gap situations, and are not explicitly formulated as a function of part deviations. To compute D P , two different methodologies will be presented and confronted. The first one is based on an optimization algorithm and Monte Carlo simulations. The second methodology uses system reliability methods. The whole approach is illustrated on a basic academic problem inspired by industrial interests.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/92972013-01-01T00:00:00ZBEAUCAIRE, PaulGAYTON, NicolasDUC, EmmanuelDANTAN, Jean-YvesOne of the aim of statistical tolerance analysis is to evaluate a predicted quality level in the design stage. A method consists in computing the defect probability D P expressed in parts per million (ppm). It represents the probability that a functional requirement will not be satisfied in mass production. This paper focuses on the statistical tolerance analysis of over-constrained mechanism with gaps. In this case, the values of the functional characteristics depend on the gap situations, and are not explicitly formulated as a function of part deviations. To compute D P , two different methodologies will be presented and confronted. The first one is based on an optimization algorithm and Monte Carlo simulations. The second methodology uses system reliability methods. The whole approach is illustrated on a basic academic problem inspired by industrial interests.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.