Predicting the martensite content of metastable austenitic steels after cryogenic turning using machine learning

During cryogenic turning of metastable austenitic stainless steels, a deformation-induced phase transformation from γ-austenite to α’-martensite can be realized in the workpiece subsurface, which results in a higher microhardness as well as in improved fatigue strength and wear resistance. The α’-martensite content and resulting workpiece properties strongly depend on the process parameters and the resulting thermomechanical load during cryogenic turning. In order to achieve specific workpiece properties, extensive knowledge about this correlation is required. Parametric models, based on physical correlations, are only partly able to predict the resulting properties due to limited knowledge on the complex interactions between stress, strain, temperature, and the resulting kinematics of deformation-induced phase transformation. Machine learning algorithms can be used to detect this kind of knowledge in data sets. Therefore, the goal of this paper is to evaluate and compare the applicability of three machine learning methods (support vector regression, random forest regression, and artificial neural network) to derive models that support the prediction of workpiece properties based on thermomechanical loads. For this purpose, workpiece property data and respective process forces and temperatures are used as training and testing data. After training the models with 55 data samples, the support vector regression model showed the highest prediction accuracy.