Hybrid multi-objective robust design optimization of a truck cab considering fatigue life
Hybrid multi-objective robust design optimization of a truck cab considering fatigue life
复制标题
考虑疲劳寿命的卡车驾驶室混合多目标稳健设计优化
DOI：
10.1016/j.tws.2021.107545
复制
发表时间：
2021-02-18
影响因子：
6.4
通讯作者：
Kim, Nam H.
中科院分区：
文献类型：
--
作者：
Qiu, Na;Jin, Zhiyang;Kim, Nam H.
在真实的生活中，不考虑设计变量的不确定性的疲劳性能优化可能是有问题的，甚至是危险的。本文提出了一种混合多目标稳健设计优化方法，使卡车驾驶室的轻量化和疲劳耐久性之间的设计适当的权衡。然而，在实际中，不确定性会导致优化设计不稳定甚至无效，这种情况在非确定性优化中会更加严重。采用田口稳健参数设计技术，细化设计变量的区间，为后续优化的基础上验证模拟模型对疲劳试验。比较了双多项式响应面法、双Kriging法和双径向基函数法三种双代理模型，选择精度较高的双Kriging法对质量和疲劳寿命的均值和标准差进行建模。采用多目标粒子群优化算法进行鲁棒设计。不同的权重因子的帕累托前沿进行了分析，提供了一些有见地的信息优化设计。稳健优化结果表明，优化后的驾驶室结构在提高疲劳寿命的同时，质量显著降低，对不确定性的敏感性降低。基于三种不同的归一化技术（线性，向量和LMM）和三种MCDM方法（TOPSIS，WPM和WSM），可以从相同的Pareto前沿获得不同的最优解。对比分析强调了归一化和MCDM方法选择在优化设计选择过程中的重要性。
Fatigue performance optimization without considering uncertainties of design variables can be problematic or even dangerous in real life. In this paper, a hybrid multi-objective robust design optimization methodology is proposed to make a proper tradeoff between the lightweight and fatigue durability for the design of a truck cab. However, the uncertainties, in reality, could lead to the optimized design unstable or even useless; this situation can be more serious in non-deterministic optimization. The Taguchi robust parametric design technique is adopted to refine the intervals of design variables for the subsequent optimization based on the validated simulation model against fatigue tests. Three types of dual surrogate models, namely the dual polynomial response surface, dual Kriging, and dual radial basis function methods are compared, and the dual Kriging is selected to model the mean and standard deviation of the mass and fatigue life for its high accuracy. The multi-objective particle swarm optimization algorithm is utilized to perform robust design. The Pareto fronts with different weight factors are analyzed to provide some insightful information on optimum designs. The robust optimization results demonstrate that the optimized design improves the fatigue life and reduces the mass of the truck cab significantly and becomes less sensitive to uncertainty. Different optimums can be obtained based on three different normalization techniques (Linear, vector, and LMM) and three MCDM methods (TOPSIS, WPM, and WSM) from the same Pareto front. The comparison analysis emphasizes the importance of normalization and MCDM method selection in the optimal design selection process.