The knowledge of the martensite start (Ms) temperature of steels is sometimes important during parts and structures fabrication, and it can not be always properly estimated using conventional empirical methods. The additions in newly developed steels of alloying elements not considered in the empirical relationships, or with compositions out of the bounds used to formulate the equations, are common problems to be solved by experimental trial and error. If the trial process was minimised, cost and time might be saved. This work outlines the use of an artificial neural network to model the calculation of Ms temperature in engineering steels from their chemical composition. Moreover, a physical interpretation of the results is presented.

It has been broadly reported that determination of the martensite start temperature in steels, M s , requires a complete description of their chemical composition. Recently, several neural networks models considering both chemical composition and austenite grain size (AGS) have been developed. Such models predict a moderate dependence of M s with AGS. The present work examines the validity of existing neural network models, but focusing on fine AGS (below 5 m).

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