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使用ANOVA机器学习和NSGA II进行AA2024-T3RFSSW参数的综合多目标优化

Piotr Myśliwiec¹, Andrzej Kubit²

¹Department of Materials Forming and Processing, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959, Rzeszów, Poland. p.mysliwiec@prz.edu.pl.

Scientific reports

|October 31, 2025

概括

这项研究优化了使用机器学习和进化算法对合金的补充摩擦动点 (RFSSW). 这些发现突显了潜入深度对于在智能制造中最大化接强度至关重要.

科学领域:

材料科学与工程材料科学与工程
制造业制造技术制造技术
计算科学计算科学

背景情况:

智能制造要求精确控制复杂的工艺,如充填摩擦点 (RFSSW),以确保产品质量.
过程参数之间的非线性相互作用显著影响固态连接的结果.
为AA2024-T3合金等材料优化RFSSW对于先进的应用至关重要.

研究的目的:

开发一个数据驱动的方法来优化RFSSW参数.
确定影响联合负载能力的关键过程变量.
为了实现AA2024-T3合金中最大接强度的多目标优化.

主要方法:

采用33个实验的全因数设计,收集关于旋转速度,入深度和接时间的数据.
评估了六种机器学习技术来预测关节负载能力,XGBoost表现出卓越的性能.
用NSGA-II进化算法进行多目标优化,生成最佳参数集的帕雷托边界.

主要成果:

入深被确定为影响接强度的最有影响的参数,由ANOVA和SHAP分析证实.
对于联合负载能力,XGBoost模型实现了高预测准确性 (R2高达0.89).
在帕雷托边界的最大化策略为最佳RFSSW参数提供了强大的妥协解决方案.

结论:

综合方法结合了统计分析,机器学习和进化优化,有效地完善了固态连接过程.
这种方法为需要多目标优化的智能制造应用提供了一个可扩展的模板.
精确预测和优化RFSSW参数对于提高产品质量和工艺效率至关重要.

关键词:

合金 2024-T3 合金网格搜索搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格超参数优化超参数优化机器学习是机器学习.多层感知子 (MLP-ANN) 是一个多层感知子.补充摩擦点接 (RFSSW) 的方法响应表面方法 (RSM)接参数优化接参数优化在XGBoost中使用.

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