Wang Xin, Xiong Wei
Physical Metallurgy and Materials Design Laboratory, Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA.
Sci Technol Adv Mater. 2020 Sep 11;21(1):626-634. doi: 10.1080/14686996.2020.1808433.
Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational tools for modeling it. In this work, we applied both thermodynamic modeling and machine learning to predict the stacking fault energy (SFE) for more than 300 austenitic steels. The comparison indicates a high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams) databases and interfacial energy prediction to enhance thermodynamic model reliability. The ensembled machine learning algorithms provide a more reliable prediction compared with thermodynamic and empirical models. Based on the statistical analysis of experimental results, only Ni and Fe have a moderate monotonic influence on SFE, while many other elements exhibit a complex effect that their influence on SFE may change with the alloy composition.
堆垛层错能（SFE）是控制奥氏体钢变形机制和优化其力学性能的最关键微观结构属性，然而目前尚无精确且简便的计算工具对其进行建模。在本研究中，我们运用热力学建模和机器学习方法预测了300多种奥氏体钢的堆垛层错能（SFE）。对比结果表明，迫切需要改进低温相图计算（CALPHAD）数据库和界面能预测，以提高热力学模型的可靠性。与热力学模型和经验模型相比，集成机器学习算法提供了更可靠的预测。基于实验结果的统计分析，只有Ni和Fe对SFE有适度的单调影响，而许多其他元素表现出复杂的效应，它们对SFE的影响可能会随合金成分而变化。
