Prediction of friction coefficient and torque in self-lubricating polymer radial bearings produced by additive manufacturing: A machine learning approach

• Published in: Proceedings of the Institution of Mechanical Engineers. Part J, Journal of engineering tribology Journal of engineering tribology, 2023-11, Vol.237 (11), p.2014-2038
• Main Authors: Baş, Hasan, Karabacak, Yunus Emre
• Format: Article
• Language: English
• Subjects: ABS resins; Acrylonitrile butadiene styrene; Additive manufacturing; Artificial neural networks; Coefficient of friction; Design optimization; Friction; Machine learning; Manufacturing; Mechanical engineering; Polyether ether ketones; Polylactic acid; Radial bearings; Regression analysis; Regression models; Self lubricating bearings; Self lubrication; Support vector machines; Torque; Tribology
• ISSN: 1350-6501; 2041-305X
• DOI: 10.1177/13506501231196355

Additive manufacturing is a rapidly developing technology that enables the production of complex parts with intricate geometries. Self-lubricating radial bearings are one of the machine elements that can be produced using additive manufacturing. In this research, we present a machine learning-based approach to model the friction coefficient and friction torque in self-lubricating radial bearings manufactured by additive manufacturing using polyether ether ketone (PEEK), polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and nylon. The proposed approach includes different machine learning models (artificial neural networks, support vector machines, regression trees, linear regression models) that utilize experimental data to predict the coefficient of friction and friction torque of different types of radial bearings. Experimental data were obtained by performing tribological tests on self-lubricating radial bearings under various operating conditions. The results reveal that the machine learning models are successful in predicting the friction coefficient and friction torque in self-lubricating radial bearings with high accuracy. The approach can be utilized to optimize the design and performance of self-lubricating radial bearings manufactured using additive manufacturing.