一个改进的多目标灰狼优化器用于单点增量成型Al1060板材的双目标参数优化.
Xiaojing Zhu1, Xinyue Zhang1, Jianhai Jiang2
1School of Mechanical & Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Materials (Basel, Switzerland)
|February 13, 2026
概括
这项研究优化了Al1060板材的单点增量成型 (SPIF),减少了稀薄和偏差. 使用机器学习和高级优化的新框架显著提高了成型质量.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造过程 制造过程 制造过程
- 计算力学 计算力学 计算力学
背景情况:
- 单点增量成型 (SPIF) 是一种灵活的金属板制造工艺.
- 过度的薄板和几何偏差是SPIF的关键挑战.
- 优化SPIF过程参数对于实现高质量的组件至关重要.
研究的目的:
- 开发一个双目标的过程参数优化框架,用于SPIF中的Al1060板.
- 为了最大限度地减少板材稀薄和几何偏差同时.
- 为了提高预测准确性和成型质量的效率.
主要方法:
- 利用多层感知子 (MLP) 作为替代模型,快速预测成型质量.
- 采用了改进的多目标灰狼优化 (IMOGWO) 算法,以有效地搜索帕雷托最佳解决方案.
- 整合了SPM混乱映射,增强了融合系数更新,以及IMOGWO内部的关联式学习.
- 应用加权的TOPSIS来从帕雷托前线中选择最佳参数.
主要成果:
- 拟议的框架有效地减少了SPIF的薄板和几何偏差.
- 通过MLP代用模型,可以快速准确地预测成型质量.
- IMOGWO算法有效地确定了双目标优化的帕雷托最佳解决方案.
- 实验验证证证实了模拟结果的低相对误差 (稀释为0.58%,偏差为3.10%).
结论:
- 开发的优化框架对于提高SPIF质量是可行的和有效的.
- 结合MLP和IMOGWO,为复杂的制造流程优化提供了一种强大的方法.
- 这些发现有实际潜力提高SPIF组件的精度和可靠性.
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