Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning

Abstract

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.

arXiv comment14 pages, 2 tables, and 4 figures

References in corpus (2)

• XGBoost: A Scalable Tree Boosting System
• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Cited by in corpus (3)

• Towards Stacking Fault Energy Engineering in FCC High Entropy Alloys
• Uncertainty Quantification and Composition Optimization for Alloy Additive Manufacturing Through a CALPHAD-based ICME Framework
• Grain-level effects on in-situ deformation-induced phase transformations in a complex-phase steel using 3DXRD and EBSD