Enhancing Constitutive Description of 5A06 Aluminum Alloy During Warm Deformation Using Machine Learning-Assisted...

To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson-Cook (ML-JC) frameworks based on artificial neural network (ANN) surrogate models. Two predictive frameworks-the parallel-decoupled PD-ANN-JC and the multi-objective integrated MOI-ANN-JC-were constructed. Quantitatively, both developed ML-JC frameworks achieve significantly higher stress prediction accuracy and superior generalization capability compared with the conventional JC model. Specifically, on the testing set, the MOI-ANN-JC framework yields an average absolute relative error (AARE) of 1.424% and an R² of 0.997, outperforming the PD-ANN-JC framework (AARE of 3.246%, R² of 0.988). On the validation set, the MOI-ANN-JC framework also demonstrates exceptional generalization, with an AARE of 3.302% and an R² of 0.987. Scientifically, the superior performance of the MOI-ANN-JC framework stems from its ANN-mnδ surrogate model, which simultaneously predicts the strain hardening exponent n, thermal softening exponent m, and relative error δ directly from deformation parameters. This mutual coupling establishes an intrinsic correlation between m and n, successfully aligning with the physical reality wherein strain hardening and thermal softening are inherently linked during deformation. Qualitatively and practically, by integrating the MOI-ANN-JC framework into finite element (FE) simulation software, dynamic tracking and visualization of the thermal softening exponent m during warm deformation were achieved. Combined with FE simulations, Vickers hardness testing and EBSD observations, this study successfully establishes a direct qualitative spatial correspondence between low-m regions and macroscopic defects, which was further verified through the warm forging of a thin-walled dual-cavity component. Crucially, this approach for evaluating deformation stability bridges the gap caused by the inapplicability of conventional processing maps within this temperature regime, offering a robust and broadly applicable workflow for complex forming optimization.