Soft computing applied to gas-assisted injection molding process optimization

张响; 童水光; 李倩; 王利霞; 董金虎

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Soft computing applied to gas-assisted injection molding process optimization

更新时间：2025-12-10

- Soft computing applied to gas-assisted injection molding process optimization
- CIESC Journal Issue 2, Pages: 508-513(2008)
- 作者机构：
  
  浙江大学化工机械研究所，郑州大学国家橡塑模具工程研究中心，陕西理工学院材料科学与工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 February 2008，
  
  Published：05 February 2008
- 稿件说明：
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张响, 童水光, 李倩, et al. Soft computing applied to gas-assisted injection molding process optimization[J]. CIESC Journal, 2008, (2): 508-513.
DOI：

张响, 童水光, 李倩, et al. Soft computing applied to gas-assisted injection molding process optimization[J]. CIESC Journal, 2008, (2): 508-513. DOI：

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

Abstract

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.

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