Classification of process conditions in martensitic stainless steel: A machine learning approach on magnetic Barkhausen emission signals

Sorting of materials at different stages of manufacturing processes is very important to ensure the final product quality. In this paper, the machine learning algorithms are used on the magnetic Barkhausen emission (MBE) signals for the classification of martensitic stainless-steel specimens, which are subjected to different heat treatments. The variation in MBE parameters, such as root mean square, peak height, peak position, and full width at half maximum, was found to be marginal for normalized and quenching and partitioned specimens even though their x-ray diffraction analysis showed distinct microstructural conditions. The conventional MBE parameters are used to train the machine learning models. The performance of decision tree and ensemble learning techniques, such as Bagging, Random Subspace, AdaBoost, RUSBoost, Total Boost, and LP Boost classifiers, is compared and found that the AdaBoost classifier provides the maximum accuracy of 98% in the classification of complex materials. The results showed that the machine learning approach using a limited number of features is sufficiently sensitive toward the classification of specimens that are otherwise indistinguishable in their conventional MBE response.