摘要
气体辅助注射成型由于气体的引入使工艺更为复杂,增加了工艺变量,参数选取更为困难。本文基于CAE数值模拟试验结果,采用软计算方法,集成人工神经网络和生物进化遗传算法优化成型工艺,实现了气体辅助注射成型试验样品气体穿透长度的最大化。数值模拟与试验结果一致。
Compared with the traditional injection molding, gas-assisted injection molding is more complicated and involves more process parameters. Process optimization becomes more difficult. Artificial neural network （ANN） and genetic algorithm （GA） were integrated to optimize the process for maximizing the gas penetration length. The simulation and experiment results were in good agreement.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2008年第2期508-513,共6页
CIESC Journal
基金
国家自然科学基金项目(10372095)~~
关键词
软计算
气辅成型
人工神经网络
遗传算法
工艺优化
soft computing
gas-assisted injection molding
artificial neural network
genetic algorithm
process optimization
