STM Journals Peer-reviewed research from Consortium e-Learning Network

Recent Trends in Fluid Mechanics Review Article

Integrating AI and ML in Tribology: A Review of Current Trends and Future Prospects

  1. Darshan Kulkarni Dept. of Mechanical Engineering SPPU University, Pune
  2. A. B. Kakade Dept. of Mechanical Engineering SPPU University, Pune

Abstract

This review paper explores the growing integration of artificial intelligence (AI) and machine learning (ML) within the field of tribology. Tribology, the study of friction, wear, and lubrication, is crucial for Improving the performance and longevity of mechanical systems. This review explores the role of AI and machine learning techniques, including artificial neural networks (ANNs), support vector machines (SVMs), and physics-informed machine learning (PIML)can be used to solve difficult tribological problems. This research presents the development of an intelligent scheduler website that streamlines task management through automation, real-time notifications, and priority-based allocation. User testing confirmed significant improvements in productivity, time efficiency, and conflict reduction compared to traditional methods. By integrating cloud-based storage and cross- platform accessibility, the system ensures flexibility and security. While effective in its current form, future enhancements such as mobile applications, third-party integrations, and advanced customization will further expand its adaptability for personal, professional, and organizational use.

Keywords

References (12)

  1. Walker J, Questa H, Raman A, Ahmed M, Mohammadpour M, Bewsher SR, et al. Application of Tribological Artificial Neural Networks in Machine Elements. Tribology Letters. 2022;71(1). doi:10.1007/s11249-022-01673-5
  2. Mahadeshwara MR, Kumar S, Dastidar AG. Artificial intelligence in the tribology: review. In: Lecture Notes in Electrical Engineering. Springer; 2023. p. 351-67. doi:10.1007/978-981-19- 5482-5_31.
  3. Rosenkranz A, Marian M, Profito FJ, Aragon N, Shah R. The Use of Artificial Intelligence in Tribology—A Perspective. Lubricants. 2020;9(1):2. doi:10.3390/lubricants9010002
  4. Yin N, Yang P, Liu S, Pan S, Zhang Z. AI for tribology: Present and future. Friction. 2024;12(6):1060-1097. doi:10.1007/s40544-024-0879-2
  5. Mohammed AJ, Mohammed AS, Mohammed AS. Prediction of Tribological Properties of UHMWPE/SiC Polymer Composites Using Machine Learning Techniques. Polymers. 2023;15(20):4057. doi:10.3390/polym15204057
  6. Seid Ahmed Y. Optimizing Femtosecond Texturing Process Parameters Through Advanced Machine Learning Models in Tribological Applications. Lubricants. 2024;12(12):454. doi:10.3390/lubricants12120454
  7. Marian M, Tremmel S. Physics-Informed Machine Learning—An Emerging Trend in Tribology. Lubricants. 2023;11(11):463. doi:10.3390/lubricants11110463
  8. Marian M, Tremmel S. Recent Advances in Machine Learning in Tribology. Lubricants. 2024;12(5):168. doi:10.3390/lubricants12050168
  9. Kałużny J, Świetlicka A, Wojciechowski Ł, Boncel S, Kinal G, Runka T, et al. Machine Learning Approach for Application-Tailored Nanolubricants’ Design. Nanomaterials. 2022;12(10):1765. doi:10.3390/nano12101765
  10. Desai PS, Granja V, Higgs CF. Lifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gas. Processes. 2021;9(6):922. doi:10.3390/pr9060922
  11. Pacini A, Ferrario M, Loehle S, Righi MC. Advancing tribological simulations of carbon-based lubricants with active learning and machine learning molecular dynamics. The European Physical Journal Plus. 2024;139(6). doi:10.1140/epjp/s13360-024-05348-z
  12. Johns-Rahnejat PM, Rahmani R, Rahnejat H. Current and future trends in tribological research. Lubricants. 2023 Sep 11;11(9):391.
Support