Steels classification by machine learning and Calphad methods

Steels of different classes (austenitic, martensitic, pearlitic, etc.) have different applications and characteristic areas of properties. In the present work two methods are used to predict steel class, based on the composition and heat treatment parameters: the physically-based Calphad method and data-driven machine learning method. They are applied to the same dataset, collected from open sources (mostly steels for high-temperature applications). Classification accuracy of 93.6% is achieved by machine learning model, trained on the concentration of three elements (C, Cr, Ni) and heat treatment parameters (heating temperatures). Calphad method gives 76% accuracy, based on the temperature and cooling rate. The reasons for misclassification by both methods are discussed, and it is shown that the part of them caused by ambiguity/inaccuracy in the data or limitations of the models used. For the rest of cases reasonable classification accuracy is demonstrated. We suggest that the reason of the supremacy of machine learning classifier is the small variation in the data used, which indeed does not change the steel class: the properties of steel should be insensitive to the details of the manufacturing process.