Kelleher J D, Mac Namee B, and D’Arcy A. Fundamentals of Machine Learning for Predictive Data Analytics. The MIT Press, 2015.

Marian M, Tremmel S. Current trends and applications of machine learning in tribology—A review. Lubricants 9(9): 86 (2021)

Article  Google Scholar 

Rosenkranz A, Marian M, Profito F J, Aragon N, Shah R. The use of artificial intelligence in tribology—A perspective. Lubricants 9(1): 2 (2020)

Article  Google Scholar 

Singh J, Azamfar M, Li F, Lee J. A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: Fundamentals, concepts and applications. Meas Sci Technol 32(1): 012001 (2020)

Article  Google Scholar 

Hamadache M, Jung J H, Park J, Youn B D. A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: Shallow and deep learning. JMST Adv 1(1–2): 125–151 (2019)

Article  Google Scholar 

Argatov I. Artificial neural networks (ANNs) as a novel modeling technique in tribology. Front Mech Eng 5: 30 (2019)

Article  Google Scholar 

Zhang Z N, Yin N, Chen S, Liu C L. Tribo-informatics: Concept, architecture, and case study. Friction 9(3): 642–655 (2021)

Article  Google Scholar 

Zhang Y Z, Kovalev A, Hayashi N, Nishiura K, Meng Y G. Numerical prediction of surface wear and roughness parameters during running-in for line contacts under mixed lubrication. J Tribol 140(6): 061501 (2018)

Article  Google Scholar 

Maier M, Pusterhofer M, Grün F. Wear simulation in lubricated contacts considering wear-dependent surface topography changes. Mater Today Proc 93: 563–570 (2023)

Article  Google Scholar 

Sander D E, Allmaier H, Priebsch H H, Witt M, Skiadas A. Simulation of journal bearing friction in severe mixed lubrication—Validation and effect of surface smoothing due to running-in. Tribol Int 96: 173–183 (2016)

Article  Google Scholar 

Patir N, Cheng M S. Application of average flow model to lubrication between rough sliding surfaces. J Lubr Technol 101(2): 220–229 (1979)

Article  Google Scholar 

Meng Y G, Xu J, Jin Z M, Prakash B, Hu Y Z. A review of recent advances in tribology. Friction 8(2): 221–300 (2020)

Article  Google Scholar 

Meng Y G, Xu J, Ma L R, Jin Z M, Prakash B, Ma T B, Wang W Z. A review of advances in tribology in 2020–2021. Friction 10(10): 1443–1595 (2022)

Article  Google Scholar 

Habchi W. Finite Element Modelling of Elastohydrodynamic Lubrication Problems. Wiley, 2018.

Ruggiero A, Sicilia A. Implementation of a finite element deformation model within an elasto-hydrodynamic lubrication numerical solver for a ball in socket tribopair. Front Mech Eng 8: 909156 (2022)

Article  Google Scholar 

Liu W K, Hu Y K. Finite element hydrodynamic friction model for metal forming. Numerical Meth Engineering 37(23): 4015–4037 (1994)

Article  Google Scholar 

Lubrecht A A, Venner C H, Colin F. Film thickness calculation in elasto-hydrodynamic lubricated line and elliptical contacts: The Dowson, Higginson, Hamrock contribution. Proc Inst Mech Eng Part J J Eng Tribol 223(3): 511–515 (2009)

Article  Google Scholar 

Evans C R, Johnson K L. The rheological properties of elastohydrodynamic lubricants. Proc Inst Mech Eng Part C J Mech Eng Sci 200(5): 303–312 (1986)

Article  Google Scholar 

Onions R A, Archard J F. The contact of surfaces having a random structure. J Phys D: Appl Phys 6(3): 289–304 (1973)

Article  Google Scholar 

Greenwood J A, Williamson J B P. Contact of nominally flat surfaces. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences 295(1442): 300–319 (1966)

Google Scholar 

Çetinel H, Öztürk H, Çelik E, Karlık B. Artificial neural network-based prediction technique for wear loss quantities in Mo coatings. Wear 261(10): 1064–1068 (2006)

Article  Google Scholar 

Kumar S, Priyadarshan, Ghosh S K. Statistical and artificial neural network technique for prediction of performance in AlSi10Mg-MWCNT based composite materials. Mater Chem Phys 273: 125136 (2021)

Article  Google Scholar 

Becker A, Fals H D C, Roca A S, Siqueira I B A F, Caliari F R, da Cruz J R, Vaz R F, de Sousa M J, Pukasiewicz A G M. Artificial neural networks applied to the analysis of performance and wear resistance of binary coatings Cr3C237WC18M and WC20Cr3C27Ni. Wear 477: 203797 (2021)

Article  Google Scholar 

Argatov I I, Chai Y S. Artificial neural network modeling of sliding wear. Proc Inst Mech Eng Part J J Eng Tribol 235(4): 748–757 (2021)

Article  Google Scholar 

Altay O, Gurgenc T, Ulas M, Özel C. Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms. Friction 8(1): 107–114 (2020)

Article  Google Scholar 

Ulas M, Altay O, Gurgenc T, Özel C. A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic, kernel-based, and weighted extreme learning machine. Friction 8(6): 1102–1116 (2020)

Article  Google Scholar 

Sardar S, Dey S, Das D. Modelling of tribological responses of composites using integrated ANN-GA technique. J Compos Mater 55(7): 873–896 (2021)

Article  Google Scholar 

Jesuthanam C P, Kumanan S, Asokan P. Surface roughness prediction using hybrid neural networks. Mach Sci Technol 11(2): 271–286 (2007)

Article  Google Scholar 

Iriaye E F, Ighravwe D E, Alade A O, Afolalu S A, Adelakun O J. Development of artificial neural network for surface roughness and machine prediction. J Phys: Conf Ser 1378: 042034 (2019)

Google Scholar 

Jones S P, Jansen R, Fusaro R L. Preliminary investigation of neural network techniques to predict tribological properties. Tribol Trans 40(2): 312–320 (1997)

Article  Google Scholar 

Baydoun S, Fartas M, Fouvry S. Comparison between physical and machine learning modeling to predict fretting wear volume. Tribol Int 177: 107936 (2023)

Article  Google Scholar 

Raissi M, Perdikaris P, Karniadakis G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378: 686–707 (2019)

Article  MathSciNet  Google Scholar 

Raissi M, Yazdani A, Karniadakis G E. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science 367(6481): 1026–1030 (2020)

Article  MathSciNet  Google Scholar 

Haghighat E, Raissi M, Moure A, Gomez H, Juanes R. A deep learning framework for solution and discovery in solid mechanics. arXiv: 2003.02751 (2020)

Rudy S, Alla A, Brunton S L, Kutz J N. Data-driven identification of parametric partial differential equations. SIAM J Appl Dyn Syst 18(2): 643–660 (2019)

Article  MathSciNet  Google Scholar 

Weeks B L, Ruddle C M, Zaug J M, Cook D J. Monitoring high-temperature solid–solid phase transitions of HMX with atomic force microscopy. Ultramicroscopy 93(1): 19–23 (2002)

Article  Google Scholar 

Haghighat E, Juanes R. SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks. Comput Meth Appl Mech Eng 373: 113552 (2021)

Article  MathSciNet  Google Scholar 

Zubov K, McCarthy Z, Ma Y B, Calisto F, Pagliarino V, Azeglio S, Bottero L, Luján E, Sulzer V, Bharambe A, et al. NeuralPDE: Automating physics-informed neural networks (PINNs) with error approximations. arXiv: 2107.09443 (2021)

Almqvist A. Fundamentals of physics-informed neural networks applied to solve the Reynolds boundary value problem. Lubricants 9(8): 82 (2021)

Article  Google Scholar 

Wang N Z, Tsai C M. Assessment of artificial neural network for thermohydrodynamic lubrication analysis. Ind Lubr Tribol 72(10): 1233–1238 (2020)

Article  Google Scholar 

Marian M, Mursak J, Bartz M, Profito F J, Rosenkranz A, Wartzack S. Predicting EHL film thickness parameters by machine learning approaches. Friction 11(6): 992–1013 (2023)

Article  Google Scholar 

Nascimento R G, Viana F A C. Fleet prognosis with physics-informed recurrent neural networks. In Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT). Chang F K, Kopsaftopoulos F, Eds., 2019: doi: https://doi.org/10.12783/shm2019/32301.

Nascimento R G, Viana F A C. Cumulative damage modeling with recurrent neural networks. AIAA J 58(12): 5459–5471 (2020)

Article  Google Scholar 

Dourado A, Viana F A C. Physics-informed neural networks for missing physics estimation in cumulative damage models: A case study in corrosion fatigue. J Comput Inf Sci Eng 20(6): 061007 (2020)

Article  Google Scholar 

Magoulas G D, Vrahatis M N. Adaptive algorithms for neural network supervised learning: A deterministic optimization approach. Int J Bifurcation Chaos 16(7): 1929–1950 (2006)

Article  MathSciNet  Google Scholar 

Oussar Y, Dreyfus G. How to be a gray box: Dynamic semi-physical modeling. Neural Netw 14(9): 1161–1172 (2001)

Article  Google Scholar 

Forssell U, Lindskog P. Combining semi-physical and neural network modeling: An example of Its usefulness. IFAC Proc Vol 30(11): 767–770 (1997)

Article  Google Scholar 

Haviez L, Toscano R, El Youssef M, Fouvry S, Yantio G, Moreau G. Semi-physical neural network model for fretting wear estimation. J Intell Fuzzy Syst Appl Eng Technol 28(4): 1745–1753 (2015)

Google Scholar 

Haykin S. Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, 1998.

Bergstra J, Bardenet R, Bengio Y, Kégl B. Algorithms for hyper-parameter optimization. In Proceedings of the 24th International Conference on Neural Information Processing Systems, 2011: 2546–2554.

Kingma D P, Ba J L. Adam: A method for stochastic optimization. ArXiv:https://arxiv.org/abs/1412.6980v9(2023)

Bair S, Winer W O. A rheological model for elastohydrodynamic contacts based on primary laboratory data. J Lubr Technol 101(3): 258–264.

Hsu S M, Munro R G, Shen M C, Gates R S. Boundary Lubricated Wear. In: Wear—Materials, Mechanisms and Practice. Stachowiak G, Ed. Chichester (UK), John Wiley & Sons Ltd., 2014: 37–70.

Google Scholar 

Walker J, Questa H, Raman A, Ahmed M, Mohammadpour M, Bewsher S R, Offner G. Application of tribological artificial neural networks in machine elements. Tribol Lett 71(1): 1–16 (2022)

Google Scholar 

Archard J F. Contact and rubbing of flat surfaces. J Appl Phys 24(8): 981–988 (1953)

Article  Google Scholar 

Hsu S M. Boundary lubrication of materials. MRS Bull 16(10): 54–58 (1991)

Article  MathSciNet  Google Scholar 

Hsu S M, Shen M C, Ruff A W. Wear prediction for metals. Tribol Int 30(5): 377–383 (1997)

Article  Google Scholar 

Friedrich K, Reinicke R, Zhang Z. Wear of polymer composites. Proc Inst Mech Eng Part J J Eng Tribol 216(6): 415–426 (2002)

Article