Application of Machine Learning in Heat Treatment Process Design of Carburized Steel

ABSTRACT The heat treatment process of carburized steel is crucial for determining its service performance potential. To overcome the efficiency and accuracy bottlenecks inherent in traditional development methods, this paper proposes a machine‐learning‐based approach for performance prediction and process optimization. This study constructed a high‐dimensional database encompassing chemical composition, physical properties, and multi‐stage heat treatment process parameters. With hardness gradient and coefficient of friction (COF) as prediction targets, the DT algorithm was selected through multi‐model comparison. Through feature selection, an optimized model was developed, achieving prediction errors of 2.7% and 4.3% for hardness and COF, respectively. Furthermore, the SHAP method was used for model interpretability analysis, identifying critical process parameters such as quenching/tempering temperatures. The optimized process design, based on this approach, was validated through physical experiments: hardness prediction error was below 6%, and the predicted COF trend highly matched the measured results. The study demonstrates that this method can accurately predict performance and guide process optimization, exhibiting excellent potential for engineering applications.