REGRESSION USING MACHINE LEARNING AND NEURAL NETWORKS FOR STUDYING TRIBOLOGICAL PROPERTIES OF WEAR-RESISTANT LAYERS

Abstract

Artificial intelligence is becoming commonplace in various research and industrial fields. In tribology, various statistical and predictive methods allow an analysis of numerical data in the form of tribological characteristics and surface structure geometry, to mention just two examples. With machine learning algorithms and neural network models, continuous values can be predicted (regression), and individual groups can be classified. In this article, we review the machine learning and neural networks application to the analysis of research results in a broad context. Additionally, a case study is presented for selected machine learning tools based on tribological tests of padding welds, from which the tribological characteristics (friction coefficient, linear wear) and wear indicators (maximum wear depth, wear area) were determined. The study results were used in exploratory data analysis to establish the correlation trends between selected parameters. They can also be the basis for regression analysis using machine learning algorithms and neural networks. The article presents a case study using these approaches in the tribological context and shows their ability to accurately and effectively predict selected tribological characteristics.