A Synergistic Framework for Hardness Prediction and Design of High-Entropy Alloys Based on Deep Learning and Intelligent Optimization Algorithms

A synergistic framework combining deep learning with intelligent optimization algorithms has been proposed to predict the hardness and optimize the composition of Al-Ti-Co-Cr-Fe-Ni system high-entropy alloys (HEAs). This forward prediction model systematically refines and selects candidate features through correlation analysis, solid solution strengthening theory, and the NSGA-III algorithm. A hybrid deep learning model integrating Transformer attention mechanisms with a multilayer perceptron (MLP) has been developed, enabling high-accuracy prediction of high-entropy alloy hardness (R²=0.9813, RMSE=10.23577). Furthermore, SHAP analysis was employed to investigate the causal links between features and hardness. An inverse design model based on the Egret Swarm Optimization Algorithm (ESOA) was applied to perform reverse optimization of the forward model, achieving optimal compositional combinations for the specified hardness targets. Validation through Laser Metal Deposition (LMD) experiments demonstrated that the hardness of the designed alloys matched well with the predicted results, with deviations below 10%. In conclusion, the overall framework for the prediction and design of HEA properties was systematically summarized.