基于迭代学习控制模型的覆盖件模具拉深筋优化算法

提出了基于迭代学习控制模型的覆盖件模具拉深筋优化算法,极大地提高了优化效率。利用成形状态函数,成形质量函数和学习律函数构建工艺参数优化的迭代控制模型。将该模型应用到拉深筋阻力值优化中,利用有限元模拟代替很难显示表达的状态函数,预测给定工艺参数方案下板料成形后的应力应变状态。根据单元的应变状态,定义拉深筋线段的局部缺陷程度为成形质量函数,评价拉深筋周围的成形质量好坏。学习律函数不仅参考拉深筋段周围的成形质量偏差确定拉深筋阻力值的改变量,同时还能智能更新学习增益修正拉深筋阻力值的改变幅度,加快了优化收敛速度。通过门内板的算例,证明了该拉深筋优化算法的快速性和实用性。

A new optimization algorithm for drawbead on panel die based on iterative learning control model

A new optimization algorithm of drawbead on panel die was proposed based on an iterative learning control model,which greatly improved the optimization efficiency.The iterative learning control model was constructed by the forming state function,the forming quality function and the learning updating law function.It was applied to the optimization of drawbead restraining force,and the state function,which was hardly calculated explicitly,was replaced by the numerical simulation to predict the stress-strain state under the given technological parameters.Furthermore,the defect degree of drawbead was defined as the forming quality function to evaluate the forming quality around drawbead.The learning updating law function not only could show the change value of drawbead restraining force based on the forming quality around drawbead,but also could update the learning gain automatically to modify the change extent of drawbead restraining force,which accelerated the convergence speed of the optimization.At last,the rapidity and practicality of the algorithm were verified by the numerical experiment of the inner door panel.