均匀设计法、神经网络和遗传算法结合在内高压成形工艺参数优化中的应用

内高压成形技术是以轻量化和一体化为特征的一种空心变截面轻体构件的先进制造技术。目前,内高压成形技术越来越受到人们的关注,特别是汽车制造企业。管材的内高压成形过程与很多因素有关,其中施加在管件内部的压力与轴向进给量之间的配比关系尤为重要,对两者的匹配关系进行优化是内高压成形面临的重要课题。传统的优化方法需要大量的模拟计算,耗时多且不易掌握。针对这一问题,该文提出了将均匀设计法、神经网络和遗传算法相结合进行参数优化,既利用了均匀设计试验的均匀可靠性,又运用神经网络的非线性映射、网络推理和预测功能,最后发挥遗传算法的全局优化特性,得出了最优结果,并直接为实际生产提供了可靠的参数依据。

Application of combining uniform design, neural network and genetic algorithm to optimize process parameters of internal high pressure forming

Internal high pressure forming is an advanced technology characterized by lightweight and integrity, which can be used to manufacture hollow structural components and "Vari-Form" parts. At present, people pay more attention to internal high pressure forming, especially some automobile enterprises. The process of tube hydroforming relates to many factors, among which the matching relation between the internal pressure and axial feeding is particularly important. The optimization of the matching relation is an important subject to internal high pressure forming. Traditional optimization methods need a lot of simulation. These methods are time-consuming and hard to be put into practice. Aiming for this problem, this paper proposes using combination of uniform design, neural network and genetic algorithm to optimize process parameters. This method uses not only the reliability of uniform design and the nonlinear mapping, network reasoning and predicting of the neural networks, but also the global optimal characteristics of genetic algorithm. Finally, the optimal result can be found out and offers a reliable parameter basis for production directly.