A crystal plasticity-informed data-driven model for magnesium alloys

Funding Sponsor

National Natural Science Foundation of China

Fourth Author's Department

Mechanical Engineering Department

Find in your Library

https://doi.org/10.1016/j.ijplas.2025.104480

All Authors

Ding Tang Shikun Qi Kecheng Zhou May Haggag Xiaochuan Sun Dayong Li Huamiao Wang Peidong Wu

Document Type

Research Article

Publication Title

International Journal of Plasticity

Publication Date

11-1-2025

doi

10.1016/j.ijplas.2025.104480

Abstract

In the past few years, data-driven models based on artificial neural network (ANN) have been successfully developed and applied to investigate the macro- and micro-mechanical behaviors of various materials. However, these data-driven models are either too complex in structure or lack interpretable physical insights. In the present work, a crystal plasticity-informed data-driven (CPIDD) model is proposed, which updates the microstructural information and parameters associated with the macroscopic constitutive model using a parallel ANN structure, and combines conventional constitutive equations to obtain the stress-strain response, ensuring efficient and stable calculations. In conjunction with the finite element (FE) method, the FE-CPIDD model simulates the micro- and macro-mechanical behaviors of magnesium (Mg) alloys under uniaxial loading, non-proportional loading, four-point bending and unloading. The comparison between the simulations and available experiments (or crystal plasticity simulations) demonstrates the accuracy and effectiveness of the proposed CPIDD model. Using Mg alloys as a representative case, the CPIDD model provides an operational and extensional tool for the design, fabrication, manufacturing, and service of the metallic components.

Link to Full Text
Find this item online

Share

COinS