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Collaborative robust optimization under uncertainty based on generalized dynamic constraints network
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DOI：10.1007/s00158-008-0271-2.png)
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Abstract
En 中文
Uncertainties exist in every aspect of a collaborative multidisciplinary design process. These uncertainties will have a great influence on design negotiations between various disciplines and may force designers to make conservative decisions. In this paper, a novel collaborative robust optimization (CRO) method based on constraints network under uncertainty is presented. The generalized dynamic constraints network (GDCN) is developed to analysis and management of uncertainties, and to ensure the parameter consistency in the collaborative design process. Given the feasible consistent parameter region, The CRO is formulated as a multi-criteria optimization problem, which brings both the objective robustness and the feasibility robustness of the constraint into account simultaneously. The CRO based on GDCN could bring both the design parameters dynamic consistency management and robust optimization into account simultaneously, which assures a product's reliability and quality robustness. The efficiency of proposed method is evaluated in the design of crank and connecting rod in one V6 engine.
Keywords:
Uncertainty
Knowledge
Collaborative robust optimization
Constraints network
Consistency
Journal
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4
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