Hot deformation behavior investigation of heat-resistant aluminum matrix composite based on Arrhenius model and machine learning

The heat-resistant aluminum matrix composite (AMC) exhibits excellent thermal performance due to the presence of heat-resistant dispersed nano-phases. Accurately characterizing high-temperature flow stress is essential for comprehending the mechanisms of deformation and improving material workability. To enhance the accuracy of modeling the flow stress for a new heat-resistant AMC during high-temperature processing, a set of isothermal compression tests at elevated temperatures was conducted. This testing was performed on the composite under varying temperature levels (473, 523, 573, 623, and 673 K) and distinct strain rates (0.001, 0.01, and 0.1 s<sup>-1</sup>). To accurately characterize the flow stress of the composite material at high temperatures, three distinct models were devised: (1) an Arrhenius model that includes strain compensation; (2) a back-propagation neural network (BPNN) model; and (3) a BPNN model optimized using a genetic algorithm (GA-BPNN). The strain compensation theory enhances the Arrhenius model’s ability to capture nonlinear characteristics, while the genetic algorithm (GA) optimizes the BPNN model’s parameter settings. The accuracy of each model in describing flow stress was compared to determine their effectiveness. The findings demonstrate that the GA-BPNN model achieved superior fitting accuracy, with a root mean square error (RMSE) of 6.48, accompanied by a coefficient of determination (R<sup>2</sup>) of 0.991 and a mean absolute error (MAE) of 5.4. To evaluate the generalization capabilities of the three models, a new data set was utilized for verification. The generalization capabilities of the three models were verified using a set of new data. The GA-BPNN model demonstrates outstanding generalization capability, achieving the highest prediction accuracy for new datasets, with R<sup>2</sup> = 0.9102, RMSE = 9.09, and MAE = 7.83. Using the GA-BPNN model’s fitting results, a hot processing map was developed, and the optimal processing window (573 to 673 K) was identified. This study serves as a valuable reference for optimizing the processing parameters of heat-resistant AMCs and proposes a novel approach combining strain compensation and machine learning for high-temperature flow stress description. While the current framework demonstrates computational robustness, extending conclusions to composites with significantly different compositions requires further validation.