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Journal Articles

Machine Learning in Wear Prediction

Raj Shah, Nikhil Pai, Gavin Thomas, Swarn Jha, Vikram Mittal, Khosro Shirvni, Hong Liang
Journal: Journal of Tribology
Publisher: ASME
Article Type: Review Articles
J. Tribol. April 2025, 147(4): 040801.
Paper No: TRIB-24-1270
https://doi.org/10.1115/1.4066865
Published Online: November 6, 2024
View Article titled, Machine Learning in Wear Prediction
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Topics: Artificial neural networks, Machine learning, Wear, Maintenance
Journal Articles

Ultrasound-Assisted Synthesis of Novel Undecylenic Estolides: Modeling and Tribological Characterization

Prasad Sanjaykumar Sanap, Rohan Sangam Singh Thakur, Amit Prabhakar Pratap
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. January 2025, 147(1): 012201.
Paper No: TRIB-24-1052
https://doi.org/10.1115/1.4065688
Published Online: August 30, 2024
View Article titled, Ultrasound-Assisted Synthesis of Novel Undecylenic Estolides: Modeling and Tribological Characterization
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Topics: Artificial neural networks, Catalysts, Lubricants, Modeling, Response surface methodology, Temperature, Tribology, Wear, Stability, Thermogravimetry
Includes: Supplementary data
Journal Articles

Performance Analysis and Reliability Optimization of Internal Feedback Hydrostatic Rotary Table Support

Jinfeng Xie, Congbin Yang, Honglie Ma, Ying Li, Zhifeng Liu, Jun Yan
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. June 2024, 146(6): 064101.
Paper No: TRIB-23-1327
https://doi.org/10.1115/1.4064400
Published Online: January 29, 2024
View Article titled, Performance Analysis and Reliability Optimization of Internal Feedback Hydrostatic Rotary Table Support
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Topics: Bearings, Feedback, Hydrostatics, Leakage, Optimization, Pressure, Reliability, Stiffness, Stress, Film thickness
Journal Articles

Experimental Investigation and Machine Learning Modeling of Wear Characteristics of AZ91 Composites

S. S. Harish Kruthiventi, Dhanunjay Kumar Ammisetti
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. October 2023, 145(10): 101704.
Paper No: TRIB-23-1084
https://doi.org/10.1115/1.4062518
Published Online: May 25, 2023
View Article titled, Experimental Investigation and Machine Learning Modeling of Wear Characteristics of AZ91 Composites
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Topics: Artificial neural networks, Composite materials, Machine learning, Wear, Stress
Journal Articles

Prediction of Frictional Moment of Cylindrical Roller Bearing Using Experimental Data-Driven Artificial Neural Networks

Yu Hou, Xi Wang, Bihe Xu, Yangliao Geng, Qingyong Li, Di Yang
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. September 2023, 145(9): 091103.
Paper No: TRIB-22-1350
https://doi.org/10.1115/1.4062367
Published Online: May 19, 2023
View Article titled, Prediction of Frictional Moment of Cylindrical Roller Bearing Using Experimental Data-Driven Artificial Neural Networks
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Topics: Artificial neural networks, Bearings, Roller bearings, Stress, Rollers
Journal Articles

Small-Dataset Machine Learning for Wear Prediction of Laser Powder Bed Fusion Fabricated Steel

Yi Zhu, Zijun Yuan, Michael M. Khonsari, Shuming Zhao, Huayong Yang
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. September 2023, 145(9): 091101.
Paper No: TRIB-22-1333
https://doi.org/10.1115/1.4062368
Published Online: May 12, 2023
View Article titled, Small-Dataset Machine Learning for Wear Prediction of Laser Powder Bed Fusion Fabricated Steel
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Topics: Algorithms, Artificial neural networks, Lasers, Machine learning, Wear, Temperature
Journal Articles

Performance Sensitivity Analysis of Natural Mineral Silicates Filled Friction Composites Using GD-ANN Approach

Debarghya Saha, Bhabani K. Satapathy
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. December 2022, 144(12): 121202.
Paper No: TRIB-22-1166
https://doi.org/10.1115/1.4055131
Published Online: August 24, 2022
View Article titled, Performance Sensitivity Analysis of Natural Mineral Silicates Filled Friction Composites Using GD-ANN Approach
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Topics: Artificial neural networks, Braking, Composite materials, Friction, Stress, Temperature, Tribology, Wear, Sensitivity analysis, Algorithms
Includes: Supplementary data
Journal Articles

Predicting the Erosion Rate of Uni-Directional Glass Fiber Reinforced Polymer Composites Using Machine-Learning Algorithms

Ajaz Ahmed Deliwala, Koshlendra Dubey, Chandra Sekher Yerramalli
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. September 2022, 144(9): 091707.
Paper No: TRIB-22-1037
https://doi.org/10.1115/1.4054247
Published Online: May 3, 2022
View Article titled, Predicting the Erosion Rate of Uni-Directional Glass Fiber Reinforced Polymer Composites Using Machine-Learning Algorithms
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Topics: Algorithms, Artificial neural networks, Erosion, Fibers, Machine learning, Particulate matter, Glass fibers, Composite materials, Fiber reinforced plastics
Journal Articles

Development of a Machine Learning Model for Elastohydrodynamic Pressure Prediction in Journal Bearings

Nathan Hess, Lizhi Shang
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. August 2022, 144(8): 081603.
Paper No: TRIB-21-1537
https://doi.org/10.1115/1.4053815
Published Online: March 7, 2022
View Article titled, Development of a Machine Learning Model for Elastohydrodynamic Pressure Prediction in Journal Bearings
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Topics: Artificial neural networks, Journal bearings, Machine learning, Pressure, Modeling, Computer simulation, Optimization
Journal Articles

Enhanced Hybrid Model to Predict the Surface Roughness of Honed Cylinder Bore

Burhan Afzal, Xueping Zhang, Anil K. Srivastava
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. January 2022, 144(1): 011201.
Paper No: TRIB-21-1208
https://doi.org/10.1115/1.4052280
Published Online: September 24, 2021
View Article titled, Enhanced Hybrid Model to Predict the Surface Roughness of Honed Cylinder Bore
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Topics: Artificial neural networks, Surface roughness, Machine learning, Cylinders, Surface texture
Journal Articles

Triboinformatics Approach for Friction and Wear Prediction of Al-Graphite Composites Using Machine Learning Methods

Md Syam Hasan, Amir Kordijazi, Pradeep K. Rohatgi, Michael Nosonovsky
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. January 2022, 144(1): 011701.
Paper No: TRIB-20-1555
https://doi.org/10.1115/1.4050525
Published Online: April 19, 2021
View Article titled, Triboinformatics Approach for Friction and Wear Prediction of Al-Graphite Composites Using Machine Learning Methods
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Topics: Composite materials, Friction, Graphite, Metal matrix composites, Tribology, Wear, Algorithms, Machine learning, Artificial neural networks
Journal Articles

Computational Fluid Dynamics Based Mixing Prediction for Tilt Pad Journal Bearing TEHD Modeling—Part II: Implementation With Machine Learning

Jongin Yang, Alan Palazzolo
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. January 2021, 143(1): 011802.
Paper No: TRIB-19-1340
https://doi.org/10.1115/1.4047751
Published Online: August 3, 2020
View Article titled, Computational Fluid Dynamics Based Mixing Prediction for Tilt Pad Journal Bearing TEHD Modeling—Part II: Implementation With Machine Learning
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Topics: Artificial neural networks, Computational fluid dynamics, Flow (Dynamics), Machine learning, Modeling, Temperature, Journal bearings, Pressure
Journal Articles

BPNN-QSTR Models for Triazine Derivatives for Lubricant Additives

Tingting Wang, Zhan Wang, Hao Chen, Kang Dai, Xinlei Gao
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. January 2020, 142(1): 011801.
Paper No: TRIB-19-1175
https://doi.org/10.1115/1.4044850
Published Online: October 17, 2019
View Article titled, BPNN-QSTR Models for Triazine Derivatives for Lubricant Additives
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Topics: Lubricants, Pressure, Tribology, Wear, Atoms, Artificial neural networks
Journal Articles

Prediction of Friction Coefficients During Scratch Based on an Integrated Finite Element and Artificial Neural Network Method

Haibo Xie, Zhanjiang Wang, Na Qin, Wenhao Du, Linmao Qian
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. February 2020, 142(2): 021703.
Paper No: TRIB-18-1430
https://doi.org/10.1115/1.4045013
Published Online: October 17, 2019
View Article titled, Prediction of Friction Coefficients During Scratch Based on an Integrated Finite Element and Artificial Neural Network Method
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Topics: Artificial neural networks, Finite element analysis, Friction, Shear (Mechanics), Simulation, Deformation
Journal Articles

Analyses of Tribological Properties of Castor Oil With Various Carbonaceous Micro- and Nano-Friction Modifiers

Shubrajit Bhaumik, Shubhabrata Datta, S. D. Pathak
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research-Article
J. Tribol. November 2017, 139(6): 061802.
Paper No: TRIB-16-1408
https://doi.org/10.1115/1.4036379
Published Online: June 30, 2017
View Article titled, Analyses of Tribological Properties of Castor Oil With Various Carbonaceous Micro- and Nano-Friction Modifiers
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Topics: Friction, Graphene, Multi-walled carbon nanotubes, Wear, Graphite, Viscosity, Flexible manufacturing systems, Artificial neural networks, Pressure, Tribology
Journal Articles

A Neural Network Identification Technique for a Foil-Air Bearing Under Variable Speed Conditions and Its Application to Unbalance Response Analysis

Mohd Firdaus Bin Hassan, Philip Bonello
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research-Article
J. Tribol. March 2017, 139(2): 021501.
Paper No: TRIB-15-1328
https://doi.org/10.1115/1.4033455
Published Online: August 11, 2016
View Article titled, A Neural Network Identification Technique for a Foil-Air Bearing Under Variable Speed Conditions and Its Application to Unbalance Response Analysis
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Topics: Bearings, Excitation, Rotors, Artificial neural networks, Foil bearings, Displacement
Journal Articles

Experimental-Based Fault Diagnosis of Rolling Bearings Using Artificial Neural Network

R. A. Kanai, R. G. Desavale, S. P. Chavan
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research-Article
J. Tribol. July 2016, 138(3): 031103.
Paper No: TRIB-15-1316
https://doi.org/10.1115/1.4032525
Published Online: April 27, 2016
View Article titled, Experimental-Based Fault Diagnosis of Rolling Bearings Using Artificial Neural Network
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Topics: Artificial neural networks, Bearings, Molecular beam epitaxy, Vibration, Rotors, Rolling bearings, Stress, Fault diagnosis
Journal Articles

Discharge Coefficients in Aerostatic Bearings With Inherent Orifice-Type Restrictors

S. H. Chang, C. W. Chan, Y. R. Jeng
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research-Article
J. Tribol. January 2015, 137(1): 011705.
Paper No: TRIB-14-1194
https://doi.org/10.1115/1.4028737
Published Online: November 6, 2014
View Article titled, Discharge Coefficients in Aerostatic Bearings With Inherent Orifice-Type Restrictors
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Topics: Bearings, Computational fluid dynamics, Discharge coefficient, Pressure, Simulation, Artificial neural networks, Flow (Dynamics)
Journal Articles

The Study of Three-Dimensional Analysis Techniques and Automatic Classification Systems for Wear Particles

Z. Peng, T. B. Kirk
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. January 1999, 121(1): 169–176.
https://doi.org/10.1115/1.2833799
Published Online: January 1, 1999
View Article titled, The Study of Three-Dimensional Analysis Techniques and Automatic Classification Systems for Wear Particles
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Topics: Classification, Particulate matter, Wear, Machinery, Artificial neural networks, Condition monitoring, Laser scanning microscopy