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使用机器学习和XAI对机械性能进行预测钢中的谷物生长动力学
Selim Demirci1,2, Durmuş Özkan Şahin3, Sercan Demirci3
1Marmara University, Faculty of Engineering, Department of Metallurgical and Materials Engineering, Istanbul, Turkey.
PloS one
|January 16, 2026
概括
本研究引入了一种机器学习 (ML) 框架,用于预测钢粒生长动力学,改进合金设计. XGBoost模型准确地预测了颗粒大小,优化了热力学处理以获得增强的机械性能.
科学领域:
- 材料科学 材料科学 材料科学
- 金工业是金工业的一个方面.
- 计算材料科学科学 计算材料科学
背景情况:
- 优化钢的机械性能需要了解在热机械加工过程中的谷物生长动力学.
- 传统模型在构成和处理参数之间的复杂相互作用中扎.
研究的目的:
- 开发一种新的机器学习 (ML) 框架来预测奥氏体谷物生长行为.
- 利用化学成分和工艺条件进行准确的预测.
主要方法:
- 使用了1039个经过实验验证的样本的综合数据集.
- 使用了XGBoost算法,在超参数优化后实现了高R2.
- 特征选择和SHAP分析确定了影响谷物生长的关键参数.
主要成果:
- XGBoost模型获得了0.9728的R2,证明了其卓越的预测能力.
- 温度,初始粒度大小和保持时间被确定为主要因素.
- 对316L不钢的实验验证显示,预测和测量的粒度之间存在很强的一致性.
结论:
- 本研究提出了第一个综合的ML和实验方法,用于预测钢粒生长动力学.
- 开发的框架为合金设计和工艺优化提供了强大的工具.
- 未来的工作将扩大框架,包括更多的变量和合金系统.
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