A Deep Learning-Based CNN-LSTM Framework for Constitutive Parameter Inversion in Alloy Gradient-Grained Materials

Abstract

Alloy gradient-grained structures (represented by copper as a typical single-phase face-centered cubic (FCC) metal), known for their superior mechanical properties such as enhanced strength, ductility, and fatigue resistance, have become increasingly important in aerospace and automotive industries. These alloys are often fabricated using advanced processing techniques such as laser welding, electron beam melting, and controlled cooling, which induce spatial gradients in grain size and optimize material properties by overcoming the traditional strength–ductility trade-off. In this study, a deep learning-based inversion framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed to efficiently predict key constitutive parameters, such as the initial critical resolved shear stress and hardening modulus, in alloy gradient-grained structures. The model integrates spatial features extracted from strain-field sequences and grain morphology images with temporal features from loading sequences, providing a comprehensive solution for path-dependent mechanical behavior modeling. Trained on high-fidelity Crystal Plasticity Finite Element Method (CPFEM) simulation data, the proposed framework demonstrates high prediction accuracy for the constitutive parameters. The model achieves an error margin of less than 5%. This work highlights the potential of deep learning techniques for the efficient and physically consistent identification of constitutive parameters in alloy gradient-grained structures, offering valuable insights for alloy design and optimization.