Modeling of Martensite Start Temperature in Low-Carbon Steels Using Artificial Neural Networks

Reliable prediction of the martensite start (Ms) temperature is essential for understanding phase transformations and optimizing heat treatment processes in steels. However, empirical relations commonly used for Ms estimation are often limited by simplified formulations and restricted compositional ranges. In this study, 19 widely used empirical equations is systematically evaluated and compared with an artificial neural network (ANN) model for low-carbon steels (C < 0.30 wt.%). A dataset comprising 285 steel compositions with 15 alloying elements was compiled, with 171 samples used for training and 114 for testing. The optimized ANN architecture (15–10–10–1) achieved a test-set mean absolute error (MAE) of 65.49 K, representing a 35–50% reduction relative to the best-performing empirical equations. Sensitivity analysis of the trained ANN model further revealed metallurgically consistent effects of alloying elements on Ms temperature. The results identify the relative contributions of alloying elements and their combined influence on martensitic transformation behavior in low-carbon steels. Experimental validation through quenching-and-partitioning heat treatment produced the expected martensite and retained austenite microstructures.