Deep learning assisted prediction of retained austenite in the carburized layer for evaluating the wear resistance of mild steel

Abstract

The content, distribution and size of retained austenite (RA) affect its mechanical stability in carburized layers. The stability of RA plays a decisive role in cold work hardening and strain-induced martensitic transformation during sliding friction; these changes determine wear resistance. In this study, a database was established based on laser confocal metallographic images of carburized layers on 23CrNi3MoA steel after different carburizing treatments. Eight algorithms were used to identify and calculate the amounts of RA in the carburized layers. The tribolayers and wear on the surfaces that underwent three carburizing processes, P13, P15, and P17, were characterized and tested. The results showed that the U-Net algorithm with an attention module and drop block regularization was the most suitable for the database. Predictions of the RA contents of surfaces after P13, P15, and P17 treatments were 19.9%, 28.1%, and 40.1%, respectively. The errors of the predictions compared with experimental results were within 5%. The surface carburized by the P15 process contained moderate amounts of RA and had the best wear resistance because the friction strain induced the formation of nanolamellar structures and the transformation of RA to martensite. The results of this study support the use of deep learning to identify and calculate the amounts of RA in carburized layers and optimize carburizing processes of mild steel.