The Role of Machine Learning in Tribology: A Systematic Review

The machine learning (ML) approach, motivated by artificial intelligence (AI), is an inspiring mathematical algorithm that accurately simulates many engineering processes. Machine learning algorithms solve nonlinear and complex relationships through data training; additionally, they can infer previo...

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Published inArchives of computational methods in engineering Vol. 30; no. 2; pp. 1345 - 1397
Main Authors Paturi, Uma Maheshwera Reddy, Palakurthy, Sai Teja, Reddy, N. S.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.03.2023
Springer Nature B.V
Subjects
Algorithms
Artificial intelligence
Design
Energy consumption
Engineering
Fault diagnosis
Friction
Fuzzy logic
Interdisciplinary aspects
Interdisciplinary studies
Journal bearings
Lubricants & lubrication
Machine learning
Mathematical and Computational Engineering
Neural networks
Process parameters
Resource conservation
Resource utilization
Review Article
Support vector machines
Tribology
Useful life
Lubrication
Review
Friction
Wear
Machine learning
Tribology
Online AccessGet full text
ISSN1134-3060
1886-1784
DOI10.1007/s11831-022-09841-5

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Abstract The machine learning (ML) approach, motivated by artificial intelligence (AI), is an inspiring mathematical algorithm that accurately simulates many engineering processes. Machine learning algorithms solve nonlinear and complex relationships through data training; additionally, they can infer previously unknown relationships, allowing for a simplified model and estimation of hidden data. Unlike other statistical tools, machine learning does not impose process parameter restrictions and yields an accurate association between input and output parameters. Tribology is a branch of surface science concerned with studying and managing friction, lubrication, and wear on relatively interacting surfaces. While AI-based machine learning approaches have been adopted in tribology applications, modern tribo-contact simulation requires a deliberate decomposition of complex design challenges into simpler sub-threads, thereby identifying the relationships between the numerous interconnected features and processes. Numerous studies have established that artificial intelligence techniques can accurately model tribological processes and their properties based on various process parameters. The primary objective of this review is to conduct a thorough examination of the role of machine learning in tribological research and pave the way for future researchers by providing a specific research direction. In terms of future research directions and developments, the expanded application of artificial intelligence and various machine learning methods in tribology has been emphasized, including the characterization and design of complex tribological systems. Additionally, by combining machine learning methods with tribological experimental data, interdisciplinary research can be conducted to understand efficient resource utilization and resource conservation better. At the conclusion of this article, a detailed discussion of the limitations and future research opportunities associated with implementing various machine learning algorithms in tribology and its interdisciplinary fields is presented.
AbstractList The machine learning (ML) approach, motivated by artificial intelligence (AI), is an inspiring mathematical algorithm that accurately simulates many engineering processes. Machine learning algorithms solve nonlinear and complex relationships through data training; additionally, they can infer previously unknown relationships, allowing for a simplified model and estimation of hidden data. Unlike other statistical tools, machine learning does not impose process parameter restrictions and yields an accurate association between input and output parameters. Tribology is a branch of surface science concerned with studying and managing friction, lubrication, and wear on relatively interacting surfaces. While AI-based machine learning approaches have been adopted in tribology applications, modern tribo-contact simulation requires a deliberate decomposition of complex design challenges into simpler sub-threads, thereby identifying the relationships between the numerous interconnected features and processes. Numerous studies have established that artificial intelligence techniques can accurately model tribological processes and their properties based on various process parameters. The primary objective of this review is to conduct a thorough examination of the role of machine learning in tribological research and pave the way for future researchers by providing a specific research direction. In terms of future research directions and developments, the expanded application of artificial intelligence and various machine learning methods in tribology has been emphasized, including the characterization and design of complex tribological systems. Additionally, by combining machine learning methods with tribological experimental data, interdisciplinary research can be conducted to understand efficient resource utilization and resource conservation better. At the conclusion of this article, a detailed discussion of the limitations and future research opportunities associated with implementing various machine learning algorithms in tribology and its interdisciplinary fields is presented.
The machine learning (ML) approach, motivated by artificial intelligence (AI), is an inspiring mathematical algorithm that accurately simulates many engineering processes. Machine learning algorithms solve nonlinear and complex relationships through data training; additionally, they can infer previously unknown relationships, allowing for a simplified model and estimation of hidden data. Unlike other statistical tools, machine learning does not impose process parameter restrictions and yields an accurate association between input and output parameters. Tribology is a branch of surface science concerned with studying and managing friction, lubrication, and wear on relatively interacting surfaces. While AI-based machine learning approaches have been adopted in tribology applications, modern tribo-contact simulation requires a deliberate decomposition of complex design challenges into simpler sub-threads, thereby identifying the relationships between the numerous interconnected features and processes. Numerous studies have established that artificial intelligence techniques can accurately model tribological processes and their properties based on various process parameters. The primary objective of this review is to conduct a thorough examination of the role of machine learning in tribological research and pave the way for future researchers by providing a specific research direction. In terms of future research directions and developments, the expanded application of artificial intelligence and various machine learning methods in tribology has been emphasized, including the characterization and design of complex tribological systems. Additionally, by combining machine learning methods with tribological experimental data, interdisciplinary research can be conducted to understand efficient resource utilization and resource conservation better. At the conclusion of this article, a detailed discussion of the limitations and future research opportunities associated with implementing various machine learning algorithms in tribology and its interdisciplinary fields is presented.
Author Paturi, Uma Maheshwera Reddy
Reddy, N. S.
Palakurthy, Sai Teja
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  givenname: Sai Teja
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  givenname: N. S.
  surname: Reddy
  fullname: Reddy, N. S.
  organization: School of Materials Science and Engineering, Gyeongsang National University
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Review
Friction
Wear
Machine learning
Tribology
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Snippet The machine learning (ML) approach, motivated by artificial intelligence (AI), is an inspiring mathematical algorithm that accurately simulates many...
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SubjectTerms Algorithms
Artificial intelligence
Design
Energy consumption
Engineering
Fault diagnosis
Friction
Fuzzy logic
Interdisciplinary aspects
Interdisciplinary studies
Journal bearings
Lubricants & lubrication
Machine learning
Mathematical and Computational Engineering
Neural networks
Process parameters
Resource conservation
Resource utilization
Review Article
Support vector machines
Tribology
Useful life
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Title The Role of Machine Learning in Tribology: A Systematic Review
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