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利用GS理论和神经网络遗传算法函数寻优法,搭乘非线性有限元分析软件Dynaform,对轻型卡车左后侧围外板拉延成形过程工艺参数寻优,以解决该零件在成形过程中出现的破裂和过度减薄质量缺陷。将GS理论和正交试验设计相结合,获得各工艺参数组合下的最大减薄率,并对获取的数据进行灰色关联度分析,找出影响减薄率的两个主要因素,即冲压速度和压边力;基于神经网络遗传算法函数寻优模型,借助拉丁超立方抽样对选出的两个主要因素进行随机抽样,将冲压速度和压边力作为输入,最大减薄率作为输出,获得输入与输出之间的非线性映射关系,并获得BP神经网络预测结果。最后,将预测结果进行个体适应度值计算,得到全局最优解和对应输入值。对比优化前后的数值模拟结果以及实验结果可知,采用此方法所得的工艺参数组合可有效提高板料成形的性能和质量。
Using GS theory and neural network genetic algorithm function optimization method, take non-linear finite element analysis software Dynaform, to optimize the process parameters of drawing forming process of the left rear side of the light truck, in order to solve the emergence of the part in the forming process Cracked and overly thinned quality defects. The combination of GS theory and orthogonal experiment design was used to obtain the maximum reduction rate under the combination of process parameters. The gray correlation analysis of the obtained data was carried out to find out the two main factors affecting the reduction rate, namely, Blank holder force; based on neural network genetic algorithm function optimization model, using Latin hypercube sampling random sampling of the two main factors selected, the stamping speed and blank holder force as input, the maximum reduction rate as output, the input And the output of the non-linear mapping, and to obtain BP neural network prediction results. Finally, the prediction result is used to calculate the individual fitness value, and the global optimal solution and the corresponding input value are obtained. Comparing the numerical simulation results before and after optimization and the experimental results, we can see that the combination of process parameters obtained by this method can effectively improve the performance and quality of sheet metal forming.