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

[{“box”:0,”content”:”n[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n[if 2704 equals=”Yes”]n

n

Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

n[/if 2704]n

n

Year : 2025 [if 2224 equals=””]08/10/2025 at 2:27 PM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page :

n

n

nn

n

n

n

    By

    n

    [foreach 286]n

    n

    Darshan Kulkarni, A. B. Kakade,

    n t

  • n

    n[/foreach]

    n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Research Scholar, Assistant Professor, Dept. of Mechanical Engineering SPPU University, Pune, Dept. of Mechanical Engineering SPPU University, Pune, Maharashtra, Maharashtra, India, India

    2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

n

n

nThis 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.nn

n

n

n

Keywords: Artificial Intelligence, Tribology, ANN, SVM, PIML, Fuzzy Systems

n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Fluid Mechanics ]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Recent Trends in Fluid Mechanics (rtfm)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article:
nDarshan Kulkarni, A. B. Kakade. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Integrating AI and ML in Tribology: A Review of Current Trends and Future Prospects[/if 2584]. Recent Trends in Fluid Mechanics. 08/10/2025; 12(03):-.

n

How to cite this URL:
nDarshan Kulkarni, A. B. Kakade. [if 2584 equals=”][226 striphtml=1][else]Integrating AI and ML in Tribology: A Review of Current Trends and Future Prospects[/if 2584]. Recent Trends in Fluid Mechanics. 08/10/2025; 12(03):-. Available from: https://journals.stmjournals.com/rtfm/article=08/10/2025/view=0

nn

n

n[if 992 equals=”Open Access”]Full Text PDF[/if 992]n

n

n[if 992 not_equal=”Open Access”]n

n

n[/if 992]n

nn

nnn

n[if 379 not_equal=””]nn

Browse Figures

n

n

n[foreach 379]

figures

[/foreach]n

n

n

n[/if 379]

n

n

n

n

n

References n

n[if 1104 equals=””]n

  1. Walker J, et al. Application of tribological artificial neural networks in machine elements. Tribol Lett. 2023 Feb;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. 2021 Jan;9(1):1-11. doi: 10.3390/lubricants9010002.
  4. Yin N, Yang P, Liu S, Pan S, Zhang Z. AI for tribology: present and future. Tsinghua Univ. 2024 Jun 1. 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 (Basel). 2023 Oct;15(20). doi: 10.3390/polym15204057.
  6. Seid Ahmed Y. Optimizing femtosecond texturing process parameters through advanced machine learning models in tribological applications. Lubricants. 2024 Dec;12(12). doi: 10.3390/lubricants12120454.
  7. Marian M, Tremmel S. Physics-informed machine learning—an emerging trend in tribology. Lubricants. 2023 Nov 1;11(11). doi: 10.3390/lubricants11110463.
  8. Marian M, Tremmel S. Recent advances in machine learning in tribology. Lubricants. 2024 May 1;12(5). doi: 10.3390/lubricants12050168.
  9. Kałużny J, et al. Machine learning approach for application-tailored nanolubricants’ design. Nanomaterials. 2022 May;12(10). 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 Jun;9(6). 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. Eur Phys J Plus. 2024 Jun;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.

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

n

n

n

n

n

Recent Trends in Fluid Mechanics

n

[if 344 not_equal=””]ISSN: 2455-1961[/if 344]

n

n

n

nn

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n[if 1746 equals=”Retracted”]n

n

n

n

[/if 1746]n[if 4734 not_equal=””]

n

n

n

[/if 4734]n

n

Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 14/05/2025
Accepted 18/07/2025
Published 08/10/2025
Retracted
Publication Time 147 Days

n

n

170

nn

n

Login

n
My IP
n

PlumX Metrics

nn

n

n

n[if 1746 equals=”Retracted”]n

[/if 1746]nnn

nnn”}]