A combined EBSD and machine learning study of predicting deformation twinning in BCC Fe81Ga19 alloy

Abstract

Compared to face-centered cubic (FCC) metals, body-centered cubic (BCC) metals demonstrate more intricate deformation behavior and microstructural characteristics because of the influence of intrinsic unstable stacking faults and defect structure. To investigate the unique phenomenon of deformation twinning in Fe81Ga19 alloy with BCC structure at moderate temperature and low strain rate, four twin nucleation models were constructed using a combination of electron backscatter diffraction (EBSD) technique and machine learning. These models are aimed to predict twin nucleation of grains and explore the relationship between twin nucleation and microstructural attributes. The study revealed that amongst the 235 grains analyzed during in-situ compressive deformation, 32 grains experienced twinning. Nine attributes influencing twin nucleation were selected and ranked according to their importance. Grain area, average image quality, neighboring grain count, and the Schmid factor of twinning systems exhibited significant influence on twin nucleation. Decision tree and tree ensemble (XGBoost) achieved 94% and 96% accuracy, respectively, on the test set, showcasing robust generalization capability. Due to the optimization of hyperparameters, support vector machine (SVM) and artificial neural network (ANN) exhibited outstanding performance. The ANN model achieved an accuracy of 97% and an F1 score of 0.91 on the test set, while the SVM model achieved an accuracy of 98% and an F1 score of 0.94, indicating superior performance. Furthermore, the study revealed that the twinning stress in Fe81Ga19 alloy exhibited weak responsiveness to temperature during deformation, and the intrinsic factors of grains were identified as crucial factors influencing twin nucleation.