摆线齿轮成形磨削机床立柱的多级优化研究

针对摆线轮成形磨床立柱优化过程中的复杂性,提出一种综合拓扑优化、结构仿生、尺寸优化的多级多目标优化设计方法,实现了立柱质量、一阶固有频率、最大应力及变形位移综合目标优化。以应变能最小为目标函数,以体积分数为约束,采用拓扑优化设计方法,确定了复杂载荷下摆线轮成形磨床立柱材料的最佳分布。以王莲叶脉和芭蕉叶柄为仿生目标,采用结构仿生优化设计方法,在立柱内部仿生空间设计仿生筋板,最大限度提升立柱整体性能。采用响应面模型遗传算法对立柱内部复杂结构尺寸进行优化,得到尺寸最优解。结果表明:在受载相同的情况下,优化后的立柱质量减轻18.4%,立柱最大应力减少23.6%,变形位移减少0.6%,一阶固有频率有所提升。

Dynamic finite element model updating using meta-model and genetic algorithm

Current dynamic finite element model updating methods are not efficient or restricted to the problem of local optima. To circumvent these, a novel updating method which integrates the meta-model and the genetic algorithm is proposed. Experimental design technique is used to determine the best sampling points for the estimation of polynomial coefficients given the order and the number of independent variables. Finite element analyses are performed to generate the sampling data. Regression analysis is then used to estimate the response surface model to approximate the functional relationship between response features and design parameters on the entire design space. In the fitness evaluation of the genetic algorithm, the response surface model is used to substitute the finite element model to output features with given design parameters for the computation of fitness for the individual. Finally, the global optima that corresponds to the updated design parameter is acquired after several generations of evolution. In the application example, finite element analysis and modal testing are performed on a real chassis model. The finite element model is updated using the proposed method. After updating, root-mean-square error of modal frequencies is smaller than 2%. Furthermore, prediction ability of the updated model is validated using the testing results of the modified structure. The root-mean-square error of the prediction errors is smaller than 2%.

Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm

To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.

采用聚乳酸复合材料的汽车零件多材料优化设计

以玄武岩纤维为增强材料,增强聚乳酸拉伸力学性能使其满足汽车非金属产品的力学性能要求,同时利用多材料方法设计产品,实现轻量化。首先研究了玄武岩纤维含量与其复合材料的关系,通过多材料设计方法并以零件的实际工况为设计条件对产品进行再设计。利用多岛遗传算法(MIGA)与遗传算法-响应面(GA-RSM)模型结合的优化方法寻求最优材料参数组合。结果表明:玄武岩纤维可提高聚乳酸的力学性能,GA-RSM模型在预测值时有较高的精确性,通过以上设计方法,使零件质量降低了9%。以上结果证明了聚乳酸复合材料可作为石油基材料的替代品应用到汽车产品研发中;多材料设计结合MIGA的方法可实现汽车产品轻量化。