|About this Abstract
||Materials Science & Technology 2020
||Artificial Intelligence for Materials Design and Process Optimization
||Stacking Fault Energy Prediction for Austenitic Steel: A Machine Learning Approach Aided by Thermodynamic Model
||Xin Wang, Wei Xiong
|On-Site Speaker (Planned)
Stacking fault energy (SFE) plays an important role in the secondary deformation mechanism and mechanical properties of austenitic steels. An appropriate SFE will lead to the Transformation-induced plasticity/Twinning-induced plasticity and overcome the trade-off between strength and ductility. However, due to the complexity in the relationship between composition and SFE, there are no accurate and simple computational tools for modeling it. To solve this problem, we evaluate the CALPHAD-based thermodynamic models (CALPHAD: calculations of phase diagrams), and generate key attributes based on thermodynamic model to aid the machine learning (ML) algorithms get higher accuracy in predicting SFE. The results show that the thermodynamic-ML jointed model is more accurate and flexible than the existing models.
||Planned: Publication outside of MS&T