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An Optimal Design of Vehicle Swing Door Using Metamodeling Techniques

机译:使用元形化技术的车辆摆动门的最佳设计

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In side-closures’ design, mass reduction provides numerous benefits in addition to reduced cost. This paper presents a Meta model based non-linear durability optimization to develop a lightweight structure for vehicle swing door. A surrogate model developed is using Kriging methodology and the thickness of the door components are given as input design variables. Adaptive Multi-Objective Genetic Algorithm (AMGA), a nonlinear optimization technique, is used in this study, to formulate the mass minimization under durability constraints. The optimized swing door design shows the overall mass saving of ~10% over initial design in terms of frame and sag deflection. The present investigation shows better effectiveness and practical applicability to develop the lightweight structure for the vehicle swing door. From the comparative study, Kriging method is found to be more effective in terms of measuring the accuracy, robustness and efficiency of the results than the Radial basis function (RBF).
机译:在侧闭的设计中,除了降低成本之外,大气减少提供了许多好处。本文介绍了基于元模型的非线性耐久性优化,为车辆摆动门开发了一种轻量级结构。开发的代理模型正在使用Kriging方法,门组件的厚度作为输入设计变量给出。自适应多目标遗传算法(AMGA),非线性优化技术,在这项研究中使用,配制下的耐久性约束的质量最小化。优化的挥杆门设计显示框架和下垂偏转方面的初始设计的整体质量节省〜10%。本研究表现出更好的有效性和实际适用性,可以为车辆摆动门开发轻质结构。从比较研究中,发现Kriging方法在测量结果的准确性,稳健性和效率方面比径向基函数(RBF)更有效。

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