Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Abstract

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition. This characteristic results in a diverse range of flow curves that vary with a deformation condition. This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning (ML) with data augmentation. The developed model combines four key strategies from data science: learning the entire flow curves, generative adversarial networks (GAN), algorithm-driven hyperparameter tuning, and gated recurrent unit (GRU) architecture. The proposed model, namely GAN-aided GRU, was extensively evaluated for various predictive scenarios, such as interpolation, extrapolation, and a limited dataset size. The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions. The GAN-aided GRU results were superior to those of previous ML models and constitutive equations. The superior performance was attributed to hyperparameter optimization, GAN-based data augmentation, and the inherent predictivity of the GRU for extrapolation. As a first attempt to employ ML techniques other than artificial neural networks, this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

Keywords

• Plastic anisotropy
• Compression
• Annealing
• Machine learning
• Data augmentation