International Journal of Mechanics and Design
Entropy-Based Damage Accumulation in Tribological Systems: A Comprehensive Review
Department of Mechanical Engineering, Institute of Engineering & Technology, Devi Ahilya Vishwavidyalaya, Indore, Madhya Pradesh, India
Abstract
This paper reviews classical wear models and entropy-based degradation approaches used for predicting failure in tribological systems. Various wear models, thermodynamic frameworks, and applications of the Degradation Entropy Generation (DEG) theorem are discussed. Friction, which is the primary source of heat generation in tribological contacts, is responsible for material degradation through a variety of irreversible processes. While classical wear approaches have been widely used for wear prediction and life estimation, they often fail to account for the underlying thermodynamic mechanisms associated with degradation. Entropy-based methodologies provide a physically meaningful framework by incorporating energy dissipation and irreversible thermodynamic effects into failure prediction.
This review critically examines classical wear models, thermodynamic degradation theories, entropy-based damage models, rolling contact fatigue approaches, and condition-monitoring applications for bearings and rotating machinery. Comparative analyses between classical and entropy-based methods are presented together with relevant mathematical formulations. In addition, current challenges, limitations, and future research opportunities associated with entropy-based degradation modelling are discussed. Comparative assessments of wear prediction methodologies and failure prediction approaches are also provided to highlight the potential of entropy-based techniques for next-generation tribological health monitoring and reliability assessment.
References (32)
- [1] J.F. Archard, Contact and rubbing of flat surfaces, J. Appl. Phys. 24 (8) (1953) 981–988. https://doi.org/10.1063/1.1721448.
- [2] R. Holm, Electric Contacts, Almqvist & Wiksells, Stockholm, Sweden, 1946.
- [3] H. Meng, K.C. Ludema, Wear models and predictive equations: Their form and content, Wear 181–183 (1995) 443–457. https://doi.org/10.1016/0043-1648(95)90158-2.
- [4] G.W. Stachowiak, A.W. Batchelor, Engineering Tribology, 4th ed., Butterworth-Heinemann, Oxford, 2013.
- [5] D. Dowson, History of Tribology, Professional Engineering Publishing, London, 1998.
- [6] M.D. Bryant, M.M. Khonsari, F.F. Ling, On the thermodynamics of degradation, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464 (2096) (2008) 2001–2014. https://doi.org/10.1098/rspa.2007.0371
- [7] M. Amiri, M.M. Khonsari, On the thermodynamics of friction and wear—A review, Entropy 12 (5) (2010) 1021–1049. https://doi.org/10.3390/e12051021.
- [8] M. Amiri, M. Modarres, An entropy-based damage characterization, Entropy 16 (12) (2014) 6434–6463. https://doi.org/10.3390/e16126434.
- [9] Jiachun Lin, Xinyu Fan, Peng Wang, Yunfei Li, Zhaoyao Shi, Ulf Olofsson, Gear wear prediction based on the theorem of degradation entropy generation, Tribology International, Volume 191,2024,109175,ISSN 0301-679X
- [10] J. Choudhry, A. Almqvist, R. Larsson, Improving Archard's Wear Model: An Energy-Based Approach, Tribology Letters 72 (3) (2024) 93. https://doi.org/10.1007/s11249-024-01856-7
- [11]Gobind, N.Grover, J.Parshad, Techniques of Measuring Wear for Bulk Materials and Advanced Surface Coatings, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) 12 (2) (2015) 101–106. https://doi.org/10.9790/1684-1226101106
- [12] Klamecki BE. An entropy-based model of plastic deformation energy dissipation in sliding. Wear 1984;96(3):319–29.
- [13] I. Prigogine, Introduction to Thermodynamics of Irreversible Processes, 3rd ed., Wiley, NewYork, 1967.
- [14] S.R. de Groot, P. Mazur, Non-Equilibrium Thermodynamics, Dover Publications, New York,1984.
- [15] J. Lemaitre, A Course on Damage Mechanics, Springer, Berlin, 1996. https://doi.org/10.1007/978-3-642-18255-6.
- [16] Peter J Blau, The significance and use of the friction coefficient, Tribology International, Volume 34, Issue 9, 2001, Pages 585-591, ISSN 0301-679X, https://doi.org/10.1016/S0301-679X(01)00050-0..
- [17] S.C. Lim, M.F. Ashby, Wear-mechanism maps, Acta Metall. 35 (1) (1987) 1–24. https://doi.org/10.1016/0001-6160(87)90209-4.
- [18] E. Ioannides, T.A. Harris, A new fatigue life model for rolling bearings, ASME J. Tribol. 107(3) (1985) 367–378. https://doi.org/10.1115/1.3261081.
- [19] G. Lundberg, A. Palmgren, Dynamic capacity of rolling bearings, Acta Polytech. Mech. Eng.Ser. 1 (1947) 7–50.
- [20] S.W.J. Welch, Thermodynamics of irreversible processes, Phys. Rep. 132 (2–3) (1986) 71–134. https://doi.org/10.1016/0370-1573(86)90025-5.
- [21] Doelling KL, Ling FF, Bryant MD, Heilman BP. An experimental study of the correlation between wear and entropy flow in machinery components. J Appl Phys 2000;88(5):2999–3003.
- [22] Ling F.F., Bryant M.D, Doelling K.L. On irreversible thermodynamics for wear prediction(2002) Wear, 253 (11-12) , pp. 1165-1172.
- [23] Takabi J, Khonsari MM. Experimental testing and thermal analysis of ball bearings. Tribol Int 2013;60:93–103.
- [24] T.A. Harris, M.N. Kotzalas, Rolling Bearing Analysis, 5th ed., CRC Press, Boca Raton, 2006.
- [25] E.V. Zaretsky, Rolling Bearing Life Prediction, Theory, and Application, NASA Reference Publication, NASA, 2013.
- [26] K.P. Lijesh, M.M. Khonsari, Characterization of multiple wear mechanisms through entropy, Tribology International, Volume 152, 2020, 106548, ISSN 0301-679X, https://doi.org/10.1016/j.triboint.2020.106548.
- [27] Lijesh, K.P., Khonsari, M.M. On the Degradation of Tribo-components in Boundary and Mixed Lubrication Regimes. Tribol Lett 67, 12 (2019). https://doi.org/10.1007/s11249-018-1125-8.
- [28] S. Aghababaei, D.H. Warner, J.F. Molinari,Critical length scale controls adhesive wear mechanisms, Nature Communications 7 (2016) 11816. https://doi.org/10.1038/ncomms11816
- [29] Sylvain Collet, Jean-François Molinari, Stella Brach,Variational phase-field continuum model uncovers adhesive wear mechanisms in asperity junctions, Journal of the Mechanics and Physics of Solids, Volume 145, 2020, 104130, ISSN 0022-5096, https://doi.org/10.1016/j.jmps.2020.104130.
- [30] Tao, Fei & Qi, Qinglin. (2019). Make more digital twins. Nature. 573. 490-491. 10.1038/d41586-019-02849-1.
- [31] Raissi, Maziar & Perdikaris, Paris & Karniadakis, George. (2018). Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics. 378. 10.1016/j.jcp.2018.10.045.
- [32] Rosenkranz, A.; Marian, M.;Profito, F.J.; Aragon, N.; Shah, R. The Use of Artificial Intelligence in Tribology—A Perspective. Lubricants 2021, 9, 2. https://dx.doi.org/ 10.3390/lubricants9010002