Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope

The efficiency of processes involving frictional contacts between surfaces is often characterized by wear rates or friction coefficients. However, the classification and forecasting of wear rates in friction related processes is a real industrial challenge that is unsolved today. Hence, an on-line monitoring system able to classify wear rate can be crucial for many industries as it could help in preventing catastrophic failures. Applications include lifetime assessment of various industrial components where a range of wear failures occur such as scuffing (a typical sudden failure mechanism). These tribological processes can now be sensorized, and the corresponding sensor signatures can be modelled and monitored using state-of-the-art Machine learning (ML) algorithms. In this study, we use an Acoustic Emission (AE) sensor and ML frameworks to classify different wear categories simulated with a customized pin-on-disc tribometer. A real-time investigation of the wear track is necessary to find out the origins of the wear scar visible at the surface. To achieve this objective, the experiments were conducted on a pin-on-disc tribometer equipped with a Digital Holographic Microscope (DHM). Experiments were carried out using alumina and steel balls against steel discs at room temperature. Real-time DHM images of the wear track surface were recorded for each lap at the same position. An acoustic emission sensor recorded the AE signals during the complete duration of experiments. The AE signatures, in combination with the real-time DHM images, were correlated as input and ground truth labels for the ML algorithm. Several ML frameworks were compared; they are support vector machine, logistic regression, XGBoost, random forest, neural networks, k-Nearest Neighbor, quadratic discriminant analysis and Naive Bayes. The classifier was trained to differentiate the acoustic emission features of the different wear rates. Most ML algorithms had an average classification accuracy above 80%, and the highest was obtained with support vector machine (84.7%). The classification accuracy can be improved by combining two neighboring categories with limited differences in terms of wear rate. Hence, the proposed method has a significant industrial potential for in-situ and real-time quality monitoring of wear processes since it requires minimum modifications of commercially available industrial machines.