基于神经网络与遗传算法的短纤维复合材料注塑成型参数优化研究  

Parameters Optimization of Short Fiber-Reinforced Composites Injection Molding By the Combining Neural Network and Genetic Algorithm

作　　者：李昆[1] 赵刚[1] LI Kun ZHAO Gang(School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, Chin)

机构地区：[1]南昌航空大学数学与信息科学学院,南昌330063

出　　处：《南昌航空大学学报（自然科学版）》2017年第2期39-43,共5页Journal of Nanchang Hangkong University(Natural Sciences)

基　　金：江西省教育厅科学技术研究项目(GJJ160711)

摘　　要：收缩是衡量制品质量的一个重要指标。本研究提出了将神经网络和遗传算法相结合的方法用于短纤维复合材料注塑制品体积收缩率优化的思路;并以体积收缩率为目标函数,以纤维长径比、纤维含量、模具温度、熔体温度、保压时间、保压压力等参数为设计变量,寻求体积收缩率最小化。基于正交试验设计,利用Moldflow软件对短纤维增强复合材料注塑成型进行数值模拟。在模拟结果的基础上,应用神经网络构建各参数与收缩率的关系模型,并通过遗传算法优化参数,得出最优参数组合。根据优化参数对一制品进行验证,减小了制品体积收缩率。Products shrinkage is an important index in measuring part quality. A method of combining neural network and genetic algorithm was proposed to optimize the volumetric shrinkage in the short fiber-reinforced composite injection molding process. In this study,the objective function is a minimum problem of the volumetric shrinkage. The design parameters include fiber aspect ratio,fiber content,mold temperature,melt temperature,holding time and holding pressure. On the basis of orthogonal experiment design,Moldflow software is applied in the short fiber-reinforced composites injection molding process. A neural network model is developed on the basis of the simulation results to map the complex nonlinear relationship between design parameters and the volumetric shrinkage. The GA is interfaced with this predictive model to improve the volumetric shrinkage significantly by optimizing the design parameters. The optimized parameter combination is obtained. Finally,the part volumetric shrinkage is eliminated by using the optimized parameters.

关 键 词：注塑成型 体积收缩率 正交试验设计 神经网络 遗传算法 

分 类 号：TQ320.662[化学工程—合成树脂塑料工业]