基于集成算法的注射成型过程中薄壁连接器的多目标优化.
Size Peng1, Mingbo Tan1, Daohong Zhang2
1State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China.
Materials (Basel, Switzerland)
|May 14, 2025
概括
这项研究通过将拉丁式超立方体采样 (LHS),模拟和DBO-BP神经网络相结合,优化了薄壁连接器的注射成型. 该方法有效地减少了曲和收缩,提高了连接器质量和生产效率.
科学领域:
- 制造业 工程 制造工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 形是通过注射成型制造薄壁连接器制造的一个重大挑战,影响了尺寸精度.
- 现有的方法很难始终达到所需的形状一致性和质量.
研究的目的:
- 开发和验证注塑成型工艺参数的综合优化方法.
- 为了最大限度地减少曲面和体积收缩,同时管理薄壁连接器中的紧力.
主要方法:
- 使用拉丁式超立方样本 (LHS) 进行实验设计.
- 采用数值模拟来评估过程参数的影响.
- 开发了一个DBO-BP神经网络,以提高预测准确度.
- 应用NSGA-II用于多目标优化,以确定最佳的过程参数.
主要成果:
- DBO-BP神经网络在曲面和体积收缩方面实现了5%的预测准确度.
- 确定了最佳的工艺参数,产生0.173毫米的曲面和7.5%的体积收缩.
- 保持了15.83的相对较高的紧力.
结论:
- 拟议的方法有效地优化了薄壁连接器的注塑参数.
- 在尺寸精度和形状一致性方面取得了显著的改进.
- 提高生产效率,减少制造缺陷.
相关概念视频
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