The paper compares classical models for determining the thermophysical properties of steels based primarily on empirical equations derived using linear regression methods with models created using machine learning methods. The selected investigated quantities include phase transformation temperatures, specific heat capacity, coefficient of thermal expansion. The results of both approaches are verified on the measured data by methods of thermal analysis such as differential scanning calorimetry, differential thermal analysis and dilatometry. The methods are evaluated both in terms of the accuracy of predictions and in terms of the adequacy of use for a specific purpose, or in terms of the complexity of creating and using the model.Keywords: Metallurgy, steel, properties, applications, testing methods
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