Abstract
Friction and wear between mating surfaces significantly affect the efficiency and performance of mechanical systems. Traditional tribological research relies on post-observation methods, limiting the understanding of dynamic friction behavior. In contrast, in situ monitoring provides real-time insights into evolving friction dynamics. This study employs machine learning to monitor polymer wear performance through friction noise. The predictive accuracy of various machine learning methods, including Extremely Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared for wear-type classification. Additionally, the LSBoost regression is selected as the optimal method for predicting polymer wear-rates across various temperatures. The results underscore the potential of using friction noise and machine learning for real-time wear monitoring, offering valuable insights for tribological system maintenance and failure prediction.
Issue Section:
Friction & Wear
References
1.
Yoshizawa
, H.
, Chen
, Y. L.
, and Israelachvili
, J.
, 1993
, “Fundamental Mechanisms of Interfacial Friction. 1. Relation Between Adhesion and Friction
,” J. Phys. Chem.
, 97
(16
), pp. 4128
–4140
. 2.
Dwivedi
, D. K.
, 2010
, “Adhesive Wear Behaviour of Cast Aluminium–Silicon Alloys: Overview
,” Mater. Des.
, 31
(5
), pp. 2517
–2531
. 3.
Wei
, H.
, Wenjian
, C.
, Shaoping
, W.
, and Tomovic
, M. M.
, 2018
, “Mechanical Wear Debris Feature, Detection, and Diagnosis: A Review
,” Chin. J. Aeronaut.
, 31
(5
), pp. 867
–882
. 4.
Karnavas
, Y. L.
, and Vairis
, A.
, 2011
, “Modelling of Frictional Phenomena Using Neural Networks: Friction Coefficient Estimation
,” Proceedings of the IASTED International Conference on Applied Simulation and Modelling (ASM 2011)
, Crete, Greece
, June 22–24
, pp. 54
–58
.5.
Bhushan
, B.
, Israelachvili
, J. N.
, and Landman
, U.
, 1995
, “Nanotribology: Friction, Wear and Lubrication at the Atomic Scale
,” Nature
, 374
(6523
), pp. 607
–616
. 6.
Pandiyan
, V.
, Prost
, J.
, Vorlaufer
, G.
, Varga
, M.
, and Wasmer
, K.
, 2022
, “Identification of Abnormal Tribological Regimes Using a Microphone and Semi-Supervised Machine-Learning Algorithm
,” Friction
, 10
(4
), pp. 583
–596
. 7.
Holmberg
, K.
, and Erdemir
, A.
, 2017
, “Influence of Tribology on Global Energy Consumption, Costs and Emissions
,” Friction
, 5
(3
), pp. 263
–284
. 8.
Rosenkranz
, A.
, Marian
, M.
, Profito
, F. J.
, Aragon
, N.
, and Shah
, R.
, 2020
, “The Use of Artificial Intelligence in Tribology—A Perspective
,” Lubricants
, 9
(1
), pp. 2
. 9.
Pandiyan
, V.
, 2019, “Modelling and In-Process Monitoring of Abrasive Belt Grinding Process
,” Nanyang Technological University Singapore.10.
Thankachan
, T.
, Soorya Prakash
, K.
, Kavimani
, V.
, and Silambarasan
, S.
, 2021
, “Machine Learning and Statistical Approach to Predict and Analyze Wear Rates in Copper Surface Composites
,” Met. Mater. Int.
, 27
(2
), pp. 220
–234
. 11.
Varga
, M.
, Grundtner
, R.
, Maurer
, A.
, and Kirchgaßner
, M.
, 2019
, “Online Wear Measurement in Harsh Environment. Part 2: Application Roller Press
,” Tribol. Schmierungstech.
, 66
(4-5
), pp. 35
–43
.12.
Sawyer
, W. G.
, and Wahl
, K. J.
, 2008
, “Accessing Inaccessible Interfaces: In Situ Approaches to Materials Tribology
,” MRS Bull.
, 33
(12
), pp. 1145
–1150
. 13.
Li
, X.
, Fu
, P.
, Chen
, K.
, Lin
, Z.
, and Zhang
, E.
, 2016
, “The Contact State Monitoring for Seal End Faces Based on Acoustic Emission Detection
,” Shock Vibr.
, 2016
(1
), p. 8726781
. 14.
Zhang
, Z.
, and Li
, X.
, 2014
, “Acoustic Emission Monitoring for Film Thickness of Mechanical Seals Based on Feature Dimension Reduction and Cascaded Decision
,” Proceedings of the 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation
, Zhangjiajie, China
, Jan. 10–11
, IEEE, pp. 64
–70
.15.
Wang
, J.
, Li
, Y.
, Zhao
, R.
, and Gao
, R. X.
, 2020
, “Physics Guided Neural Network for Machining Tool Wear Prediction
,” J. Manuf. Syst.
, 57
, pp. 298
–310
. 16.
Kong
, D.
, Chen
, Y.
, and Li
, N.
, 2018
, “Gaussian Process Regression for Tool Wear Prediction
,” Mech. Syst. Signal Process
, 104
, pp. 556
–574
. 17.
Wang
, J.
, Wang
, P.
, and Gao
, R. X.
, 2015
, “Enhanced Particle Filter for Tool Wear Prediction
,” J. Manuf. Syst.
, 36
, pp. 35
–45
. 18.
Wang
, J.
, Yan
, J.
, Li
, C.
, Gao
, R. X.
, and Zhao
, R.
, 2019
, “Deep Heterogeneous GRU Model for Predictive Analytics in Smart Manufacturing: Application to Tool Wear Prediction
,” Comput. Ind.
, 111
, pp. 1
–14
. 19.
Caggiano
, A.
, 2018
, “Tool Wear Prediction in Ti-6Al-4V Machining Through Multiple Sensor Monitoring and PCA Features Pattern Recognition
,” Sensors
, 18
(3
), pp. 823
. 20.
Varga
, M.
, Haas
, M.
, Schneidhofer
, C.
, and Adam
, K.
, 2020
, “Wear Intensity Evaluation in Conveying Systems—An Acoustic Emission and Vibration Measurement Approach
,” Tribol. Int.
, 149
, pp. 105549
. 21.
Lingard
, S.
, Yu
, C. W.
, and Yau
, C. F.
, 1993
, “Sliding Wear Studies Using Acoustic Emission
,” Wear
, 162
, pp. 597
–604
. 22.
Mano
, H.
, Yoshioka
, T.
, Korenaga
, A.
, and Yamamoto
, T.
, 2000
, “Relationship Between Growth of Rolling Contact Fatigue Cracks and Load Distribution
,” Tribol. Trans.
, 43
(3
), pp. 367
–376
. 23.
Voronenko
, B.
, 1982
, “Acoustic Emission During Phase Transformations in Alloys
,” Met. Sci. Heat Treat.
, 24
(8
), pp. 545
–553
. 24.
Simmons
, J.
, and Wadley
, H.
, 1984
, “Theory of Acoustic Emission From Phase Transformations
,” J. Res. Nat. Bur. Stand.
, 89
(1
), pp. 55
. 25.
Baranov
, V. M.
, Kudryavtsev
, E. M.
, and Sarychev
, G. A.
, 1997
, “Modelling of the Parameters of Acoustic Emission Under Sliding Friction of Solids
,” Wear
, 202
(2
), pp. 125
–133
. 26.
Hase
, A.
, Wada
, M.
, and Mishina
, H.
, 2008
, “The Relationship Between Acoustic Emissions and Wear Particles for Repeated Dry Rubbing
,” Wear
, 265
(5–6
), pp. 831
–839
. 27.
Beerbower
, A.
, 1975
, “Mechanical Failure Prognosis Through Oil Debris Monitoring
,” USAAVLABS TR74-100, U.S. Army Air Mobility Research and Development Laboratory, Fort Eustis (ADA006190).28.
Hase
, A.
, Mishina
, H.
, and Wada
, M.
, 2012
, “Correlation Between Features of Acoustic Emission Signals and Mechanical Wear Mechanisms
,” Wear
, 292
, pp. 144
–150
. 29.
Pandiyan
, V.
, and Tjahjowidodo
, T.
, 2019
, “Use of Acoustic Emissions to Detect Change in Contact Mechanisms Caused by Tool Wear in Abrasive Belt Grinding Process
,” Wear
, 436
, pp. 203047
. 30.
Hase
, A.
, 2020
, “Early Detection and Identification of Fatigue Damage in Thrust Ball Bearings by an Acoustic Emission Technique
,” Lubricants
, 8
(3
), pp. 37
. 31.
Al-Dossary
, S.
, Hamzah
, R. R.
, and Mba
, D.
, 2009
, “Observations of Changes in Acoustic Emission Waveform for Varying Seeded Defect Sizes in a Rolling Element Bearing
,” Appl. Acoust.
, 70
(1
), pp. 58
–81
. 32.
Poddar
, S.
, and Tandon
, N.
, 2019
, “Detection of Particle Contamination in Journal Bearing Using Acoustic Emission and Vibration Monitoring Techniques
,” Tribol. Int.
, 134
, pp. 154
–164
. 33.
Rowe
, K. G.
, Bennett
, A. I.
, Krick
, B. A.
, and Sawyer
, W. G.
, 2013
, “In Situ Thermal Measurements of Sliding Contacts
,” Tribol. Int.
, 62
, pp. 208
–214
. 34.
He
, Z.
, Shi
, T.
, Xuan
, J.
, and Li
, T.
, 2021
, “Research on Tool Wear Prediction Based on Temperature Signals and Deep Learning
,” Wear
, 478
, pp. 203902
. 35.
Li
, Z.
, Liu
, R.
, and Wu
, D.
, 2019
, “Data-Driven Smart Manufacturing: Tool Wear Monitoring with Audio Signals and Machine Learning
,” J. Manuf. Processes
, 48
, pp. 66
–76
. 36.
D’Addona
, D. M.
, Ullah
, A. S.
, and Matarazzo
, D.
, 2017
, “Tool-Wear Prediction and Pattern-Recognition Using Artificial Neural Network and DNA-Based Computing
,” J. Intell. Manuf.
, 28
(6
), pp. 1285
–1301
. 37.
Bergs
, T.
, Holst
, C.
, Gupta
, P.
, and Augspurger
, T.
, 2020
, “Digital Image Processing With Deep Learning for Automated Cutting Tool Wear Detection
,” Procedia Manuf.
, 48
, pp. 947
–958
. 38.
Gouarir
, A.
, Martínez-Arellano
, G.
, Terrazas
, G.
, Benardos
, P.
, and Ratchev
, S.
, 2018
, “IN-Process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis
,” Procedia CIRP
, 77
, pp. 501
–504
. 39.
Shevchik
, S. A.
, Saeidi
, F.
, Meylan
, B.
, and Wasmer
, K.
, 2016
, “Prediction of Failure in Lubricated Surfaces Using Acoustic Time–Frequency Features and Random Forest Algorithm
,” IEEE Trans. Ind. Inf.
, 13
(4
), pp. 1541
–1553
. 40.
Han
, T.
, Jiang
, D.
, Zhao
, Q.
, Wang
, L.
, and Yin
, K.
, 2018
, “Comparison of Random Forest, Artificial Neural Networks and Support Vector Machine for Intelligent Diagnosis of Rotating Machinery
,” Trans. Inst. Meas. Control
, 40
(8
), pp. 2681
–2693
. 41.
Kang
, S.
, Ma
, D.
, Wang
, Y.
, Lan
, C.
, Chen
, Q.
, and Mikulovich
, V.
, 2017
, “Method of Assessing the State of a Rolling Bearing Based on the Relative Compensation Distance of Multiple-Domain Features and Locally Linear Embedding
,” Mech. Syst. Signal Process
, 86
, pp. 40
–57
. 42.
Fauvel
, K.
, Fromont
, É
, Masson
, V.
, Faverdin
, P.
, and Termier
, A.
, 2022
, “XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
,” Data Min. Knowl. Disc.
, 36
(3
), pp. 917
–957
. 43.
Zhou
, Z.-H.
, and Feng
, J.
, 2019
, “Deep Forest
,” Natl. Sci. Rev.
, 6
(1
), pp. 74
–86
. 44.
Massa
, A.
, Marcantonio
, D.
, Chen
, X.
, Li
, M.
, and Salucci
, M.
, 2019
, “DNNS as Applied to Electromagnetics, Antennas, and Propagation—A Review
,” IEEE Antennas Wirel. Propag. Lett.
, 18
(11
), pp. 2225
–2229
. 45.
James
, G.
, Witten
, D.
, Hastie
, T.
, and Tibshirani
, R.
, 2013
, An Introduction to Statistical Learning
, Springer
, New York
.46.
Moonam
, H. M.
, Qin
, X.
, and Zhang
, J.
, 2019
, “Utilizing Data Mining Techniques to Predict Expected Freeway Travel Time From Experienced Travel Time
,” Math. Comput. Simul.
, 155
, pp. 154
–167
. 47.
Zhang
, Y.
, and Xu
, X.
, 2022
, “Modulus of Elasticity Predictions Through LSBoost for Concrete of Normal and High Strength
,” Mater. Chem. Phys.
, 283
, pp. 126007
. 48.
Sergienko
, V.
, Bukharov
, S.
, and Kupreev
, A.
, 2007
, “Tribological Processes on Contact Surfaces in Oil-Cooled Friction Pairs
,” Proc. NAS Belarus
, 51
(4
), pp. 86
–89
.49.
Bukharov
, S.
, 2004
, “Reduction of Vibroacoustic Activity of Metal-Polymer Tribojoints in Nonstationary Friction Processes
,” Summary of Ph. D: Thesis, 5.50.
Ringlein
, J.
, and Robbins
, M. O.
, 2004
, “Understanding and Illustrating the Atomic Origins of Friction
,” Am. J. Phys.
, 72
(7
), pp. 884
–891
. 51.
Krim
, J.
, 2002
, “Surface Science and the Atomic-Scale Origins of Friction: What Once Was Old Is New Again
,” Surf. Sci.
, 500
(1–3
), pp. 741
–758
. 52.
Fleischer
, G.
, 1973
, “Energetische Methode der Bestimmung des Verschleißes
,” Schmierungstechnik
, 4
(9
), pp. 269
–274
.Copyright © 2024 by ASME
You do not currently have access to this content.
