基于机器学习的高度奥氏体不钢的多目标组成优化
Yinghu Wang1,2, Long Chen3, Limei Cheng2
1National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种新的工作流程,通过结合热力学计算和机器学习来设计高度奥氏体不钢 (HNASS). 该方法优化了钢结构,提高了耐腐蚀性和微观结构稳定性,抑制了不必要的阶段.
科学领域:
- 材料科学 材料科学 材料科学
- 金工业是一种金工业.
- 计算材料设计设计 计算材料设计
背景情况:
- 高度奥氏体不钢 (HNASS) 需要仔细的组成控制,以平衡耐腐蚀性和微观结构稳定性.
- 抑制诸如三角铁和沉物 (例如,Cr2N,西格玛相,M23C6碳化物) 等有害相对于最佳性能至关重要.
研究的目的:
- 为HNASS开发一个可解释的,多目标的设计工作流程.
- 将热力学建模与机器学习结合起来,用于预测钢材特性.
- 为了确定最大限度地提高耐腐蚀性 (PREN) 和微观结构稳定性的最佳成分.
主要方法:
- 用于热力学描述器 (平衡和谢尔计算) 的相图计算 (CALPHAD).
- 雇佣了机器学习代理模型 (随机森林,XGBoost),在广泛的组成数据上进行训练.
- 集成的多目标优化算法 (NSGA-III,TOPSIS) 具有基于物理的功能.
主要成果:
- 随机森林模型显示了高精度 (PREN RMSE ≈0.004) 和概括性.
- 沙普利添加物解释 (SHAP) 提供了对元素效应的金学上一致的见解.
- 为了最大限度地减少不良阶段并最大限度地提高PREN,生成帕雷托阵线,识别最佳组合窗口.
结论:
- 开发的工作流程是高效的,可重复的,可用于数据驱动的不钢设计.
- 确定了具有改善PREN和受控降水的可操作组合候选物.
- 该方法为设计先进的不钢提供了一种可转移的方法.
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