Machine-learning-assisted understanding of discontinuous precipitation behavior in Cu-Ni-Si alloy

A machine learning framework integrating both classification and regression models was developed to characterize the evolution of discontinuous precipitates (DPs) in Cu-Ni-Si alloy throughout the composition and processing conditions. The framework incorporates orthogonal experiments, data augmentation, predictive modeling, and analysis of DP behavior. The soft-voting ensemble classifier achieved a predictive accuracy of 0.98 ± 0.01 in identifying the presence of DPs, whereas the regression model attained a coefficient of determination of 0.98 on the test set for quantifying the volume fraction of DPs. Utilizing the combined predictions, two-dimensional contour maps were generated and segmented into DP-suppressed, DP-sensitive, and DP-active regions, thereby elucidating the progression of DP behavior across varying compositional and processing conditions. SHapley Additive exPlanations analysis revealed that DPs occurrence was sequentially controlled by Ni content, aging temperature (AT), Si content, and cold-rolling reduction (CR), whereas the DP volume fraction was mainly controlled by AT, followed by Ni content, CR, and Si content. This work establishes an understanding of the intrinsic interrelationships among the composition, processing parameters, and DP behavior, thereby providing new mechanistic insights for microstructural regulation and performance optimization in Cu-Ni-Si alloy.