Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms

X-ray diffractograms of high-speed steels are analyzed using machine learning algorithms to accurately classify various heat treatments. These differently heat-treated steel samples are also investigated by dilatometric analysis and by metallographic analysis in order to label the samples accordingly. Both agglomerative hierarchical clustering and t-distributed stochastic neighbor embedding are employed to automatically classify preprocessed X-ray datasets. The clusters obtained by this procedure agree well with the labeled data. By supervised learning via a support vector machine, hyperplanes are constructed that allow separating the clusters from each other based on the X-ray measurements. The exactness of these hyperplanes is analyzed by cross-validation. The machine learning algorithms used in this work are valuable tools to separate different microstructures based on their diffractograms. It is demonstrated that the separation of martensitic, bainitic, and pearlitic microstructures is possible based on the diffractograms only by means of machine learning algorithms, while the same problem is error-prone when looking at the diffractograms only.