Predicting the Coefficient of Friction in Rolling Contact Between 100Cr6 Bearing Steel Discs Using Machine Learning—Applications Within Industry 4.0/5.0

The digital transformation of manufacturing associated with Industry 4.0 and the human-centric paradigm of Industry 5.0 requires advanced predictive tools that can improve the performance, reliability, and sustainability of tribological systems. In this context, accurate prediction of friction behavior in rolling contacts is crucial for intelligent monitoring and optimization of bearing components. This study presents a machine learning-based methodology for predicting the coefficient of friction in rolling contact of 100Cr6 steel bearing discs as a function of surface roughness and rolling distance parameters. Experimental studies were conducted using discs with different surface topography under controlled rolling contact conditions. Surface roughness characteristics and rolling distance data were correlated with experimentally measured friction coefficients to create a comprehensive dataset for artificial intelligence (AI) modeling. Several dozen machine learning (ML) algorithms, including random forest, support vector regression, and artificial neural networks, were developed and comparatively evaluated to capture nonlinear relationships between operational and surface parameters. The predictive ability of the models was assessed using statistical metrics such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The obtained results demonstrate that ML methods provide high prediction accuracy and effectively identify the combined effects of surface roughness and rolling distance on rolling contact friction. Feature importance analysis revealed that roughness parameters dominate friction behavior during the run-in phase, while rolling distance becomes increasingly important under stabilized operating conditions. The proposed approach supports the development of intelligent tribological systems, predictive maintenance strategies, and data-driven decision-making frameworks aligned with Industry 4.0 and Industry 5.0 concepts. The presented methodology can contribute to the implementation of intelligent manufacturing solutions, the sustainable operation of bearing systems, and AI-assisted monitoring of machine components in modern industrial environments.