基于人工智能的冲压件回弹控制系统

Controlling the Springback of Stamping System Based on AI Model

回弹是一个多变量相互作用的高度非线性问题,至今在解析和数值方法中未能找到一个很有效的解决途径。本文以一物品盒为研究对象,建立了基于人工神经网络的从冲压工艺参数到冲压回弹量的非线性映射关系,并且运用遗传算法对冲压工艺进行优化,实现冲压回弹的预测与控制,为板料冲压材料、工艺参数优化提供了一种新的思路。

Spring-back of sheet metal forming is a highly nonlinear problem resulting from the interaction of many variables. Up to the present, there is no effective way to solute this problem by analytical method or numerical method. In this paper, taking an example for a box in vehicle, A non-linear mapping relation between stamping technology and springback, based on artificial neural networking. And optimized stamping technology by genetic algorithm,realized the spring-back forecasting and controlling. It was a new way for stamping and optimizing technology.