用机器学习驱动的非线性分析,对合金中包含效应进行分析
Arup Datta1,2, Amit Kumar Rana1, Ranjan Kumar Ghadai3
1Department of Mechanical Engineering, ICFAI University Tripura, Kamalghat, Agartala, Tripura, 799210, India.
Scientific reports
|October 14, 2025
概括
包含物对合金性能有很大的影响,尺寸是影响机械性能的关键因素. 控制包装尺寸可以大大提高强度,疲劳寿命和耐腐蚀性.
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:
- 包含是合金中的关键缺陷,影响材料性能.
- 对于工业应用来说,了解包含在机械性能上的精确影响至关重要.
研究的目的:
- 通过机器学习,全面分析包含物对合金性能的影响.
- 建立包含特征和材料性能指标之间的定量关系.
主要方法:
- 机器学习技术,包括SHAP值分析,非线性回归和集群分析.
- 使用随机森林和梯度提升算法进行比较模型评估.
- 分析拉伸强度,疲劳寿命和与包含尺寸相关的腐蚀行为.
主要成果:
- 包含尺寸是机械性能降解的主要驱动因素,超过了密度的影响.
- 非线性回归确定了影响拉伸强度的关键包含尺寸值.
- 随机森林模型展示了对材料属性的卓越预测准确性.
- 观察到强度 (25 MPa) 和疲劳寿命的显著改善,包括缩小的含量大小 (5 μm vs 10 μm).
- 腐蚀速率随着更大的包含大小而呈指数级增加.
结论:
- 机器学习为理解合金中包含性质关系提供了一个强大的框架.
- 控制包含尺寸对于提高材料性能和可靠性至关重要.
- 该研究为航空航天和汽车行业提供了可操作的质量控制参数.
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