基于Xgboost机器学习算法进行的金属-金属结合过程研究
Jingpeng Feng1,2, Lihua Zhan1,2, Bolin Ma1,2
1State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.
Polymers
|October 28, 2023
概括
优化金属-金属结合使用机器学习和有限元素模型. 这种方法通过优化过程参数,如粗度和圈长度,提高了14%的拉力剪切强度.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 优化粘合过程参数通常涉及广泛的实验和复杂的数据分析.
- 新技术对于理解过程参数和粘合性能之间的复杂关系至关重要.
研究的目的:
- 通过先进的计算和机器学习技术,提高金属对金属的粘合性能.
- 识别和优化影响粘合强度的关键过程参数.
主要方法:
- 使用单关节 (SLJ) 实验来生成数据.
- 采用有限元模型 (FEMs) 来模拟和验证结合行为.
- 应用了Xgboost机器学习 (ML) 算法进行参数优化和分析.
- 使用SHAP (Shapley添加式解释) 来解释参数的重要性.
主要成果:
- 在70次运行中训练的Xgboost模型展示了有效的预测能力.
- 拉力剪切强度 (TSS) 的参数重要性被排列为:粗度,粘合层厚度和膝盖长度.
- 优化的参数包括粗度 (0.89微米),粘合层厚度 (0.1毫米) 和膝盖长度 (27毫米).
- 实验验证显示,在优化参数的情况下,TSS增加了14%.
结论:
- 机器学习方法可以更准确,更直观地理解过程参数对TSS的影响.
- SLJ实验,FEM和ML的综合方法为优化结合过程提供了有效的途径.
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