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14,000 data sets from an industrialized bainitization process, consisting of process gas furnace, salt bath and circulating air furnace, were used to predict the resulting Vickers hardness of cylinder heads made of 100Cr6 based on process data such as temperature and pressure. For prediction, machine learning methods such as ANNs, CNNs, ensemble methods and support vector regressors were compared. Meta features such as the furnace number as well as features extracted from the recorded time series were used. Data preparation and feature extraction were performed according to the machine learning methods used. The random forest achieved the best predictions with an R 2  score of 0.406 and also allows the evaluation of the most important features.

Prediction of Hardness after Industrialized Bainitization of 100Cr6 based on Process Parameters by Application of Machine Learning Methods ∗