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Journal Articles

Integrating Friction Noise for In Situ Monitoring of Polymer Wear Performance: A Machine Learning Approach in Tribology

Shengshan Chen, Ganlin Cheng, Fei Guo, Xiaohong Jia, Xiaohao Wen

Publisher: ASME

J. Tribol. June 2025, 147(6): 061701.

Paper No: TRIB-24-1181

https://doi.org/10.1115/1.4066947

Published Online: November 13, 2024

View Article titled, Integrating Friction Noise for In Situ Monitoring of Polymer Wear Performance: A Machine Learning Approach in Tribology

Topics: Friction, Noise (Sound), Tribology, Wear, Polymers, Machine learning

Journal Articles

Machine Learning in Wear Prediction

Raj Shah, Nikhil Pai, Gavin Thomas, Swarn Jha, Vikram Mittal, Khosro Shirvni, Hong Liang

Publisher: ASME

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

Topics: Artificial neural networks, Machine learning, Wear, Maintenance

Journal Articles

Triboinformatics Approach for Prediction of High-Stress Abrasive Wear and Coefficient of Friction in Al/TiC Nanocomposites Using Machine Learning Techniques

Chitti Babu Golla, R. Narasimha Rao, Syed Ismail

Publisher: ASME

J. Tribol. February 2025, 147(2): 021401.

Paper No: TRIB-24-1245

https://doi.org/10.1115/1.4066415

Published Online: September 13, 2024

View Article titled, Triboinformatics Approach for Prediction of High-Stress Abrasive Wear and Coefficient of Friction in Al/TiC Nanocomposites Using Machine Learning Techniques

Topics: Friction, Machine learning, Nanocomposites, Stress, Wear, Abrasion, Composite materials

Journal Articles

A Novel Method for Bearing Fault Diagnosis Based on Novel Feature Sets With Machine Learning Technique

Asmita R. Mali, P. V. Shinde, Amit Prakash Patil, Vishal G. Salunkhe, R. G. Desavale, Prashant S. Jadhav

Publisher: ASME

J. Tribol. February 2025, 147(2): 024301.

Paper No: TRIB-24-1161

https://doi.org/10.1115/1.4066306

Published Online: September 13, 2024

View Article titled, A Novel Method for Bearing Fault Diagnosis Based on Novel Feature Sets With Machine Learning Technique

Topics: Algorithms, Bearings, Machine learning, Vibration, Signals

Journal Articles

Identification and Fault Diagnosis of Rolling Element Bearings Using Dimension Theory and Machine Learning Techniques

Prashant S. Jadhav, Vishal G. Salunkhe, R. G. Desavale, S. M. Khot, P. V. Shinde, P. M. Jadhav, Pramila R. Gadyanavar

Publisher: ASME

J. Tribol. September 2024, 146(9): 094301.

Paper No: TRIB-23-1388

https://doi.org/10.1115/1.4065335

Published Online: May 15, 2024

View Article titled, Identification and Fault Diagnosis of Rolling Element Bearings Using Dimension Theory and Machine Learning Techniques

Topics: Bearings, Fault diagnosis, Modeling, Vibration, Rotors, Machine learning, Rolling bearings

Journal Articles

Atomic-Scale Insights Into Graphene/Fullerene Tribological Mechanisms and Machine Learning Prediction of Properties

Feng Qiu, Hui Song, Weimin Feng, Zhiquan Yang, Ziyan Lu, Xianguo Hu

Publisher: ASME

J. Tribol. June 2024, 146(6): 062102.

Paper No: TRIB-23-1352

https://doi.org/10.1115/1.4064402

Published Online: February 13, 2024

View Article titled, Atomic-Scale Insights Into Graphene/Fullerene Tribological Mechanisms and Machine Learning Prediction of Properties

Topics: Friction, Industrial lubrication systems, Machine learning, Nanoparticles, Surface roughness, Tribology, Wear, Graphene, Pressure, Lubricants

Includes: Supplementary data

Journal Articles

Experimental Investigation of the Influence of Various Wear Parameters on the Tribological Characteristics of AZ91 Hybrid Composites and Their Machine Learning Modeling

Dhanunjay Kumar Ammisetti, S. S. Harish Kruthiventi

Publisher: ASME

J. Tribol. May 2024, 146(5): 051704.

Paper No: TRIB-23-1293

https://doi.org/10.1115/1.4064397

Published Online: January 22, 2024

View Article titled, Experimental Investigation of the Influence of Various Wear Parameters on the Tribological Characteristics of AZ91 Hybrid Composites and Their Machine Learning Modeling

Topics: Composite materials, Friction, Machine learning, Stress, Wear, Graphene, Tribology, Algorithms, Modeling

Journal Articles

Tribo-Informatics Approach to Investigate the Friction and Wear of Bushings in the Variable Stator Vane System

Ke He, Yufei Ma, Zhinan Zhang

Publisher: ASME

J. Tribol. December 2023, 145(12): 121704.

Paper No: TRIB-23-1104

https://doi.org/10.1115/1.4063186

Published Online: September 14, 2023

View Article titled, Tribo-Informatics Approach to Investigate the Friction and Wear of Bushings in the Variable Stator Vane System

Topics: Bushings, Disks, Friction, Stress, Wear, Temperature, Stators, Machine learning

Journal Articles

Automated Vibration and Acoustic Crepitus Sensing in Humans

Gregory Roytman, Jocelyn Faydenko, Matthew Budavich, Judith D. Pocius, Gregory Cramer

Publisher: ASME

J. Tribol. September 2023, 145(9): 091110.

Paper No: TRIB-23-1012

https://doi.org/10.1115/1.4062808

Published Online: July 17, 2023

View Article titled, Automated Vibration and Acoustic Crepitus Sensing in Humans

Topics: Accelerometers, Acoustics, Machine learning, Reliability, Signals, Tribology, Vibration, Errors, Lumbar spine, Phantoms

Journal Articles

Stochastic Performance of Journal Bearing With Two-Layered Porous Bush—A Machine Learning Approach

Subrata Barman, Kritesh Kumar Gupta, Subrata Kushari, Sudip Dey

Publisher: ASME

J. Tribol. October 2023, 145(10): 104101.

Paper No: TRIB-22-1271

https://doi.org/10.1115/1.4062487

Published Online: May 25, 2023

View Article titled, Stochastic Performance of Journal Bearing With Two-Layered Porous Bush—A Machine Learning Approach

Topics: Bearings, Journal bearings, Load bearing capacity, Machine learning, Pressure, Reynolds number, Leakage, Support vector machines, Simulation, Steady state

Includes: Supplementary data

Journal Articles

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

S. S. Harish Kruthiventi, Dhanunjay Kumar Ammisetti

Publisher: ASME

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

Topics: Artificial neural networks, Composite materials, Machine learning, Wear, Stress

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

Publisher: ASME

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

Topics: Algorithms, Artificial neural networks, Lasers, Machine learning, Wear, Temperature

Journal Articles

A Machine Learning Approach for Real-Time Wheel-Rail Interface Friction Estimation

Morinoye O. Folorunso, Michael Watson, Alan Martin, Jacob W. Whittle, Graham Sutherland, Roger Lewis

Publisher: ASME

J. Tribol. September 2023, 145(9): 091102.

Paper No: TRIB-22-1440

https://doi.org/10.1115/1.4062373

Published Online: May 12, 2023

View Article titled, A Machine Learning Approach for Real-Time Wheel-Rail Interface Friction Estimation

Topics: Friction, Rails, Wheels, Trains, Machine learning

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

Publisher: ASME

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

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

Publisher: ASME

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

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

Publisher: ASME

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

Topics: Artificial neural networks, Surface roughness, Machine learning, Cylinders, Surface texture

Journal Articles

Tilt Pad Bearing Distributed Pad Inlet Temperature With Machine Learning—Part I: Static and Dynamic Characteristics

Jongin Yang, Alan Palazzolo

Publisher: ASME

J. Tribol. June 2022, 144(6): 061801.

Paper No: TRIB-20-1503

https://doi.org/10.1115/1.4052171

Published Online: September 3, 2021

View Article titled, Tilt Pad Bearing Distributed Pad Inlet Temperature With Machine Learning—Part I: Static and Dynamic Characteristics

Topics: Finite element methods, Fluid films, Temperature, Rotors, Bearings, Machine learning, Flow (Dynamics), Computational fluid dynamics, Temperature distribution, Fluids

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

Publisher: ASME

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

Topics: Composite materials, Friction, Graphite, Metal matrix composites, Tribology, Wear, Algorithms, Machine learning, Artificial neural networks

Journal Articles

Classification of Friction and Wear State of Wind Turbine Gearboxes Using Decision Tree and Random Forest Algorithms

Jinshan Pang, Yuming Chen, Shizhong He, Huihe Qiu, Chili Wu, Lingbo Mao

Publisher: ASME

J. Tribol. September 2021, 143(9): 091702.

Paper No: TRIB-20-1371

https://doi.org/10.1115/1.4049257

Published Online: January 8, 2021

View Article titled, Classification of Friction and Wear State of Wind Turbine Gearboxes Using Decision Tree and Random Forest Algorithms

Topics: Adhesives, Algorithms, Cutting, Friction, Support vector machines, Wear, Wind turbines, Viscosity, Fatigue, Particulate matter

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

Publisher: ASME

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

Topics: Artificial neural networks, Computational fluid dynamics, Flow (Dynamics), Machine learning, Modeling, Temperature, Journal bearings, Pressure

Journal citation

NARROW

Supplemental Material

Integrating Friction Noise for In Situ Monitoring of Polymer Wear Performance: A Machine Learning Approach in Tribology

Machine Learning in Wear Prediction

Triboinformatics Approach for Prediction of High-Stress Abrasive Wear and Coefficient of Friction in Al/TiC Nanocomposites Using Machine Learning Techniques

A Novel Method for Bearing Fault Diagnosis Based on Novel Feature Sets With Machine Learning Technique

Identification and Fault Diagnosis of Rolling Element Bearings Using Dimension Theory and Machine Learning Techniques

Atomic-Scale Insights Into Graphene/Fullerene Tribological Mechanisms and Machine Learning Prediction of Properties

Experimental Investigation of the Influence of Various Wear Parameters on the Tribological Characteristics of AZ91 Hybrid Composites and Their Machine Learning Modeling

Tribo-Informatics Approach to Investigate the Friction and Wear of Bushings in the Variable Stator Vane System

Automated Vibration and Acoustic Crepitus Sensing in Humans

Stochastic Performance of Journal Bearing With Two-Layered Porous Bush—A Machine Learning Approach

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

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

A Machine Learning Approach for Real-Time Wheel-Rail Interface Friction Estimation

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

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

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

Tilt Pad Bearing Distributed Pad Inlet Temperature With Machine Learning—Part I: Static and Dynamic Characteristics

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

Classification of Friction and Wear State of Wind Turbine Gearboxes Using Decision Tree and Random Forest Algorithms

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

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