A machine learning approach to predict the wear behaviour of steels

One or both surfaces, which come in contact with a relative motion between them, can experience material loss due to wear. This is a complex phenomenon involving several parameters consisting of both the material and experimental conditions. It is thus very much difficult to predict the volume loss under a specific condition as a consequence of wear. In this study, an effort was given to develop a machine learning approach involving several parameters such as composition, microstructure, hardness, load, sliding distance, temperature etc to quantify and predict the material loss. The outcomes obtained from the model were found to be logical with existing knowledge. The model predictions were validated with experimental results not used to build up the model.