Machine learning based prediction of carbon concentration in carburized steel

The carbon concentration and hardness of a part's surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified.