Both conventional automobiles and new energy cars require urgently lightweight design to realize energy economy and environmental protection in a long run. The weight reduction of the body structure plays a rather important role in decreasing the weight of the full vehicle. In real engineering problems, the variation in sheet gauge, geometrical size, and material parameters caused by environmental factors and other uncertainties may affect the structural performances of body components. Therefore, a lightweight design without considering this kind of tolerance may result in the loss of feasibility and reliability in engineering application. From the viewpoint of crashworthiness performance, this paper presents a study on the lightweight design of the automotive front-body structure based on robust optimization, considering the variation in design variables including sheet gauge and yield limit of materials. Coupled with the design and analysis of a computer experiment, four metamodelling techniques, namely support vector regression, kriging, radial basis functions, and artificial neural networks, are employed to build the metamodels of structural crashworthiness performance indicators for comparison of approximation accuracy. An adaptive deterministic optimization process is used to upgrade further the approximation accuracy of metamodels for the following robust optimization. A double-loop strategy is chosen when solving the robust optimization formulation and the basic Monte Carlo simulation method is applied to perform a reliability analysis. A genetic algorithm solver is used to obtain both the deterministic and the robust optimum results for comparison. The reduced weight obtained by using robust optimization is 7.8003kgf (19.45 per cent) and the result achieved from robust optimization is more conservative than that obtained through deterministic optimization as expected. However, the robust optimum design is ensured to be feasible and reliable when the variation in design variables works in a real engineering application.
The paper discusses the architecture of a knowledge-based vehicle body conceptual assembly design system. The proposed system addresses problems when applying knowledgebased techniques to vehicle body design at the conceptual design stage and later stages. The paper mainly gives a proposed framework and organization of the conceptual assembly design system with the help of knowledge-based engineering, where it takes into integrated consideration the assembly sequence, joint configuration, and tolerance allocation in the autobody assembly design process planning. Heuristic knowledge and empirical knowledge play active roles in the generation of the assembly sequence and dimension chain, the configuration of joint types, and the allocation of tolerance limits, and qualitative simulation is knowledge reasoning or rule reasoning with all kinds of rule, criterion, and principle, so that the knowledge-based vehicle assembly design system can improve assembly concept quality by means of qualitative simulation to fulfil the concurrent integration between conceptual design and detail design stages.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.