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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
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
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
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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
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
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
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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
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
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
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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
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

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

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
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
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
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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
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

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

Jongin Yang, Alan Palazzolo
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
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
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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
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

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
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
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
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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
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