塑料科技2018，Vol.46Issue(3)：91-96,6.DOI:10.15925/j.cnki.issn1005-3360.2018.03.014
基于灰色关联度和BP神经网络的多级注塑成型工艺参数优化
Optimization for Multi-Stage Injection Process Parameters Based on Gray Relational Grade and BP Neural Network
车应田 1刘泓滨 1火寿平2
作者信息
- 1. 昆明理工大学机电工程学院,云南昆明650500
- 2. 云南开放大学机电工程学院,云南昆明650223
- 折叠
摘要
Abstract
Aiming at the problem of difficulty in installing the head hood of electric vehicle,this experiment aims to reduce the volume of warpage and the volume shrinkage of the product as the optimization target.First of all,on the basis of the orthogonal experiment,the initial optimum process parameters are obtained by using the signal to noise ratio and the grey correlation analysis.Then,based on the initial optimal technological parameters,the four factors that affect the quality of products are the orthogonal test,and the BP neural network is trained to predict the best process parameters.Finally,the CAE is used to simulate the results.The warpage is 1.540 mm under the optimum process parameters and the volume shrinkage is 6.709％,which is in line with the production requirements.The proposed optimization method can effectively improve the quality of products,shorten the production cycle,and provide a reliable solution for the optimization of multistage injection molding process parameters.关键词
电动车头罩/信噪比/灰色关联度/BP神经网络/多级注塑成型Key words
Electric hood cover/Signal-to-noise ratio/Gray relational grade/BP neural network/Multi-stage injection molding分类
信息技术与安全科学引用本文复制引用
车应田,刘泓滨,火寿平..基于灰色关联度和BP神经网络的多级注塑成型工艺参数优化[J].塑料科技,2018,46(3):91-96,6.
