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基于可解释机器学习的钢铁疲劳强度的预测
Chengcheng Liu1,2, Xuandong Wang2, Weidong Cai2
1Institute of Structural Steel, Central Iron and Steel Research Institute, Beijing 100081, China.
Materials (Basel, Switzerland)
|December 9, 2023
概括
这项研究使用机器学习来预测钢的疲劳强度 (FS). 热处理温度和含量等关键特性显著提高了疲劳性能.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 钢材疲劳失效是一个主要问题,需要分析疲劳强度 (FS).
- 提高疲劳性能需要了解影响FS的关键特征.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测钢的疲劳强度 (FS).
- 识别和分析影响FS的关键特征.
- 提高FS预测的ML模型的可解释性.
主要方法:
- 收集了钢材材料的疲劳强度数据.
- 提出并比较了三个特征构建策略与四个ML算法.
- 采用特征选择方法,包括ANOVA,嵌入式,递归式和详尽的方法.
- 使用贝叶斯优化,沙普利增量解释 (SHAP) 和符号回归 (SR).
主要成果:
- 战略III (组合,热处理,原子特征) 和梯度提升树 (GBT) 算法的组合显示出最佳性能.
- 确定了影响FS的关键特征:热处理温度 (TT),平均原子半径 (mE),原子包装指数密度 (APID) 和 (Mo) 含量.
- SHAP分析显示,TT和Mo是最具影响力的特征,最佳范围为1600.15,用于增强FS.
- SR在关键特征和FS之间建立了重要的数学关系.
结论:
- 成功开发了一种强大的ML模型,用于预测钢的疲劳强度.
- 关键的材料和加工特性显著影响疲劳强度.
- SHAP和SR为模型解释性提供了宝贵的见解,并为增强疲劳性能提供了重要的功能.
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