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Crashworthiness design of multi-component tailor-welded blank (TWB) structures

机译:多组件拼焊板(TWB)结构的耐撞性设计

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Crashworthiness of tailor-welded blank (TWB) structures signifies an increasing concern in lightweight design of vehicle. Although multiobjective optimization (MOO) has to a considerable extent been successfully applied to enhance crashworthiness of vehicular structures, majority of existing designs were restricted to single or uniform thin-walled components. Limited attention has been paid to such non-uniform components as TWB structures. In this paper, MOO of a multi-component TWB structure that involves both the B-pillar and inner door system subjected to a side impact, is proposed by considering the structural weight, intrusive displacements and velocity of the B-pillar component as objectives, and the thickness in different positions and the height of welding line of B-pillar as the design variables. The MOO problem is formulated by using a range of different metamodeling techniques, including response surface methodology (RSM), artificial neural network (ANN), radial basis functions (RBF), and Kriging (KRG), to approximate the sophisticated nonlinear responses. By comparison, it is found that the constructed metamodels based upon the radial basis function (RBF, especially multi-quadric model, namely RBF-MQ) fit to the design of experiment (DoE) checking points well and are employed to carry out the design optimization. The performance of the TWB B-pillar and indoor panel system can be improved by optimizing the thickness of the different parts and height of the welding line. This study demonstrated that the multi-component TWB structure can be optimized to further enhance the crashworthiness and reduce the weight, offering a new class of structural/material configuration for lightweight design.
机译:拼焊毛坯(TWB)结构的耐撞性表明,车辆轻量化设计受到越来越多的关注。尽管多目标优化(MOO)在很大程度上已经成功地应用于提高车辆结构的耐撞性,但大多数现有设计都局限于单个或统一的薄壁部件。对于诸如TWB结构的不均匀组件已给予了有限的关注。本文以B柱构件的结构重量,侵入位移和速度为目标,提出了同时受到B柱和内门系统影响的多构件TWB结构的MOO, B柱不同位置的厚度和焊缝高度作为设计变量。 MOO问题是通过使用一系列不同的元建模技术(包括响应面方法(RSM),人工神经网络(ANN),径向基函数(RBF)和Kriging(KRG))来制定的,以近似复杂的非线性响应。通过比较,发现基于径向基函数(RBF,尤其是多二次模型,即RBF-MQ)构造的元模型非常适合实验(DoE)检查点的设计,并被用于进行设计优化。通过优化不同零件的厚度和焊接线的高度,可以提高TWB B柱和室内面板系统的性能。这项研究表明,可以优化多组件TWB结构,以进一步提高耐撞性和减轻重量,从而为轻型设计提供了新的结构/材料配置类别。

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