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1-11 of 11
Keywords: machine learning
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Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Review Articles
J. Tribol. April 2025, 147(4): 040801.
Paper No: TRIB-24-1270
Published Online: November 6, 2024
... on wear prediction based on physical models, but due to device complexity and uncertainty, these methods often fail to provide accurate predictions and accurate wear identification. Machine learning, as a data-driven approach based on its ability to discover patterns and correlations in complex systems...
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. February 2025, 147(2): 021401.
Paper No: TRIB-24-1245
Published Online: September 13, 2024
...Chitti Babu Golla; R. Narasimha Rao; Syed Ismail This study highlights the importance of Al–Fe–Si alloys in modern engineering for their enhanced hardness, strength, and wear resistance, improving fuel efficiency in the aerospace and automotive sectors. Data-driven analysis and machine learning...
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Review Articles
J. Tribol. February 2025, 147(2): 020801.
Paper No: TRIB-24-1149
Published Online: September 13, 2024
... of diverse machine learning approaches. Email: 411918003@nitt.edu Email: sharath@kluniversity.in 1 Corresponding author. Email: harish@nitt.edu Contributed by the Tribology Division of ASME for publication in the J ournal of T ribology . 10 05 2024 22 08 2024 23 08...
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. June 2024, 146(6): 062102.
Paper No: TRIB-23-1352
Published Online: February 13, 2024
... was predicted based on six machine learning algorithms. The results indicated that in fluid lubrication, graphene promoted “liquid–liquid” interlayer sliding, whereas fullerene facilitated “solid–liquid” interface sliding, resulting in a decrease or increase in friction force. Under boundary lubrication...
Topics:
Friction,
Industrial lubrication systems,
Machine learning,
Nanoparticles,
Surface roughness,
Tribology,
Wear,
Graphene,
Pressure,
Lubricants
Includes: Supplementary data
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. September 2023, 145(9): 091110.
Paper No: TRIB-23-1012
Published Online: July 17, 2023
... recommend a machine learning study with a larger number of subjects who can better capture the nuances of varying types of human crepitus. Human crepitus signals are quite complex. Consequently, our first task was to establish a strict definition of a crepitus signal. This was a novel aspect...
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. October 2023, 145(10): 104101.
Paper No: TRIB-22-1271
Published Online: May 25, 2023
... efficiency, this probabilistic study is conducted in conjunction with the machine learning (ML) model based on the support vector machine (SVM) algorithm. The uncertainty in the bearing responses is presented in the form of the probability density function (PDF), considering both the independent and combined...
Topics:
Bearings,
Journal bearings,
Load bearing capacity,
Machine learning,
Pressure,
Reynolds number,
Leakage,
Support vector machines,
Simulation,
Steady state
Includes: Supplementary data
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. September 2023, 145(9): 091101.
Paper No: TRIB-22-1333
Published Online: May 12, 2023
... procedure and contact conditions. Machine learning offers a facile path to predict mechanical properties if sufficient datasets are available, without which it is very challenging to attain a high prediction accuracy. In this work, high-accuracy wear prediction of 316L stainless steel parts fabricated using...
Journal Articles
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
Published Online: May 12, 2023
.... , and Tremmel , S. , 2021 , “ Current Trends and Applications of Machine Learning in Tribology—A Review ,” Lubricants , 9 ( 9 ), p. 86 . 10.3390/lubricants9090086 [16] Chambers , J. M. , and Hastie , T. J. , 2017 , “ Statistical Models ,” Statistical Models in S , Routledge , UK , pp...
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. December 2022, 144(12): 121703.
Paper No: TRIB-22-1189
Published Online: September 26, 2022
... machine learning (ML) algorithms. The prediction accuracy of the data-driven models derived from ML algorithms exceeds that of the multivariate regression benchmark because the latter does not always capture the complex relationship between the as-built surface topography parameters and the corresponding...
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. August 2022, 144(8): 081603.
Paper No: TRIB-21-1537
Published Online: March 7, 2022
...Nathan Hess; Lizhi Shang This paper presents a machine learning neural network capable of approximating pressure as the distributive result of elastohydrodynamic (EHD) effects for a journal bearing at steady state. Design of efficient, reliable fluid power pumps and motors requires accurate models...
Journal Articles
Journal:
Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. January 2022, 144(1): 011701.
Paper No: TRIB-20-1555
Published Online: April 19, 2021
...Md Syam Hasan; Amir Kordijazi; Pradeep K. Rohatgi; Michael Nosonovsky Data-driven analysis and machine learning (ML) algorithms can offer novel insights into tribological phenomena by establishing correlations between material and tribological properties. We developed ML algorithms using...