Coupling Approach of Crystal Plasticity and Machine Learning in Predicting Forming Limit Diagram of AA7075-T6 at Various Temperatures and Strain Rates

Abstract

This study proposes a data-driven framework for predicting forming limit diagrams (FLDs) of AA7075-T6 aluminum sheets under various temperatures and strain rates. To overcome the limitations of costly and time-consuming experiments, a hybrid dataset combining experimental results and virtual data from rate-dependent crystal plasticity finite element (CPFE) simulations coupled with the Marciniak-Kuczy & nacute;ski (M-K) model was developed. Several machine learning (ML) models-including linear regression (LR), random forest regression (RFR), support vector regression (SVR), Gaussian process regression (GPR), and multilayer perceptron (MLP)-were trained to predict FLDs. The nonlinear dependence of the FLD on temperature and strain rate was accurately captured by the ML models, with nonlinear algorithms demonstrating notably improved predictive performance. The proposed approach offers an efficient, accurate, and cost-effective method for FLD prediction and supports data-driven process design in lightweight alloy forming.