Journal Home > Volume 12 , Issue 6
Friction 2024, 12(6): 1235-1249 https://doi.org/10.1007/s40544-023-0803-1
Research Article | Open Access | Issue | Published: 10 January 2024

Prediction of ball-on-plate friction and wear by ANN with data-driven optimization

Show Author's Information Alexander KOVALEV,  Yu TIAN,  Yonggang MENG( )
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, China
Keywords:
deep learning, machine learning, artificial neural network, mixed lubrication
Cite this article:
KOVALEV A, TIAN Y, MENG Y. Prediction of ball-on-plate friction and wear by ANN with data-driven optimization. Friction, 2024, 12(6): 1235-1249. https://doi.org/10.1007/s40544-023-0803-1
Download citation
EndNote(RIS)
BibTeX

47

Views

4

Downloads

Citations

0

Crossref

0

WoS

0

Scopus

0

CSCD

Abstract Full text About this article

For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to "learn" the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.


menu
Abstract
Full text
Outline
About this article

Prediction of ball-on-plate friction and wear by ANN with data-driven optimization

Show Author's information Alexander KOVALEV, Yu TIAN, Yonggang MENG( )
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, China

Abstract

For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to "learn" the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.

Keywords: deep learning, machine learning, artificial neural network, mixed lubrication

References(66)

[1]
Kelleher J D, Mac Namee B, and D’Arcy A. Fundamentals of Machine Learning for Predictive Data Analytics. The MIT Press, 2015.
[2]
Marian M, Tremmel S. Current trends and applications of machine learning in tribology—A review. Lubricants 9(9): 86 (2021)
DOI Google Scholar
[3]
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)
DOI Google Scholar
[4]
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)
DOI Google Scholar
[5]
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)
DOI Google Scholar
[6]
Argatov I. Artificial neural networks (ANNs) as a novel modeling technique in tribology. Front Mech Eng 5: 30 (2019)
DOI Google Scholar
[7]
Zhang Z N, Yin N, Chen S, Liu C L. Tribo-informatics: Concept, architecture, and case study. Friction 9(3): 642–655 (2021)
DOI Google Scholar
[8]
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)
DOI Google Scholar
[9]
Maier M, Pusterhofer M, Grün F. Wear simulation in lubricated contacts considering wear-dependent surface topography changes. Mater Today Proc 93: 563–570 (2023)
DOI Google Scholar
[10]
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)
DOI Google Scholar
[11]
Patir N, Cheng M S. Application of average flow model to lubrication between rough sliding surfaces. J Lubr Technol 101(2): 220–229 (1979)
DOI Google Scholar
[12]
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)
DOI Google Scholar
[13]
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)
DOI Google Scholar
[14]
Habchi W. Finite Element Modelling of Elastohydrodynamic Lubrication Problems. Wiley, 2018.
DOI
[15]
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)
DOI Google Scholar
[16]
Liu W K, Hu Y K. Finite element hydrodynamic friction model for metal forming. Numerical Meth Engineering 37(23): 4015–4037 (1994)
DOI Google Scholar
[17]
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)
DOI Google Scholar
[18]
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)
DOI Google Scholar
[19]
Onions R A, Archard J F. The contact of surfaces having a random structure. J Phys D: Appl Phys 6(3): 289–304 (1973)
DOI Google Scholar
[20]
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)
DOI Google Scholar
[21]
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)
DOI Google Scholar
[22]
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)
DOI Google Scholar
[23]
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)
DOI Google Scholar
[24]
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)
DOI Google Scholar
[25]
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)
DOI Google Scholar
[26]
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)
DOI Google Scholar
[27]
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)
DOI Google Scholar
[28]
Jesuthanam C P, Kumanan S, Asokan P. Surface roughness prediction using hybrid neural networks. Mach Sci Technol 11(2): 271–286 (2007)
DOI Google Scholar
[29]
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)
DOI Google Scholar
[30]
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)
DOI Google Scholar
[31]
Baydoun S, Fartas M, Fouvry S. Comparison between physical and machine learning modeling to predict fretting wear volume. Tribol Int 177: 107936 (2023)
DOI Google Scholar
[32]
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)
DOI Google Scholar
[33]
Raissi M, Yazdani A, Karniadakis G E. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science 367(6481): 1026–1030 (2020)
DOI Google Scholar
[34]
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)
[35]
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)
DOI Google Scholar
[36]
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)
DOI Google Scholar
[37]
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)
DOI Google Scholar
[38]
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)
[39]
Almqvist A. Fundamentals of physics-informed neural networks applied to solve the Reynolds boundary value problem. Lubricants 9(8): 82 (2021)
DOI Google Scholar
[40]
Wang N Z, Tsai C M. Assessment of artificial neural network for thermohydrodynamic lubrication analysis. Ind Lubr Tribol 72(10): 1233–1238 (2020)
DOI Google Scholar
[41]
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)
DOI Google Scholar
[42]
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
[43]
Nascimento R G, Viana F A C. Cumulative damage modeling with recurrent neural networks. AIAA J 58(12): 5459–5471 (2020)
DOI Google Scholar
[44]
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)
DOI Google Scholar
[45]
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)
DOI Google Scholar
[46]
Oussar Y, Dreyfus G. How to be a gray box: Dynamic semi-physical modeling. Neural Netw 14(9): 1161–1172 (2001)
DOI Google Scholar
[47]
Forssell U, Lindskog P. Combining semi-physical and neural network modeling: An example ofIts usefulness. IFAC Proc Vol 30(11): 767–770 (1997)
DOI Google Scholar
[48]
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)
DOI Google Scholar
[49]
Haykin S. Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, 1998.
[50]
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.
[51]
Kingma D P, Ba J L. Adam: A method for stochastic optimization. ArXiv: https://arxiv.org/abs/1412.6980v9 (2023)
Google Scholar
[52]
Bowden F P, Tabor D. 1964 The Friction and Lubrication of Solids. Oxford: Clarendon Press.
[53]
Bair S, Winer W O. A rheological model for elastohydrodynamic contacts based on primary laboratory data. J Lubr Technol 101(3): 258–264.
DOI Google Scholar
[54]
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.
DOI
[55]
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)
DOI Google Scholar
[56]
Archard J F. Contact and rubbing of flat surfaces. J Appl Phys 24(8): 981–988 (1953)
DOI Google Scholar
[57]
Hsu S M. Boundary lubrication of materials. MRS Bull 16(10): 54–58 (1991)
DOI Google Scholar
[58]
Hsu S M, Shen M C, Ruff A W. Wear prediction for metals. Tribol Int 30(5): 377–383 (1997)
DOI Google Scholar
[59]
Friedrich K, Reinicke R, Zhang Z. Wear of polymer composites. Proc Inst Mech Eng Part J J Eng Tribol 216(6): 415–426 (2002)
DOI Google Scholar
[60]
Rice J R, Ruina A L. Stability of steady frictional slipping. J Appl Mech 50(2): 343–349 (1983)
DOI Google Scholar
[61]
Bowden F P, Leben L. Nature of sliding and the analysis of friction. Nature 141: 691–692 (1938)
DOI Google Scholar
[62]
Velten K, Reinicke R, Friedrich K. Wear volume prediction with artificial neural networks. Tribol Int 33(10): 731–736 (2000)
DOI Google Scholar
[63]
Argatov I I, Chai Y S. An artificial neural network supported regression model for wear rate. Tribol Int 138: 211–214 (2019)
DOI Google Scholar
[64]
Zhang Y Z, Kovalev A, Meng Y G. Combined effect of boundary layer formation and surface smoothing on friction and wear rate of lubricated point contacts during normal running-in processes. Friction 6(3): 274–288 (2018)
DOI Google Scholar
[65]
Yao H H, Zhou Z, Wang L, Tan Z, He D Y, Zhao L D. Thermal conductivity and wear behavior of HVOF-sprayed Fe-based amorphous coatings. Coatings 7(10): 173 (2017)
DOI Google Scholar
[66]
Ruina A. Slip instability and state variable friction laws. J Geophys Res 88(B12): 10359–10370 (1983)
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 05 April 2023
Revised: 15 May 2023
Accepted: 07 July 2023
Published: 10 January 2024
Issue date: June 2024

Copyright

© The author(s) 2023.

Acknowledgements

This work was funded by the National Natural Science Foundation of China (NSFC) with Grant No. 51635009.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Return