Tribology and Machine Learning: New Perspectives and Challenges
A special issue of Lubricants (ISSN 2075-4442).
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 12133
Special Issue Editors
Interests: mechatronic systems; frictional modeling and model-based control in automotive transmissions; lubrication in internal combustion engines and journal bearings; effects of nanoparticles as friction reducer additives; vibration measurement methods
Special Issues, Collections and Topics in MDPI journals
Interests: mixed friction; fluid friction; seizure/scoring/scuffing; elastohydrodynamic lubrication; mixed lubrication/transition of lubrication regimes; journal bearings; gears/cams; joint prosthesis
Interests: tribology; biomimetics; viscoelastic materials; contact mechanics; adhesion
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) and Artificial Intelligence (AI) approaches have found their path into tribology world among other broad areas of scientific disciplines, where such novel techniques can support sorting through the complexity of patterns and identifying trends within multiple interacting features.
There have been recent advancements in the application of machine learning methods to improve the tribological behaviour of materials, machine element operation, shapes, coatings, etc. Indeed, published articles found in the literature cover many fields of tribology from novel materials to surface engineering and nanolubricants. Accordingly, the targets of the proposed numerical algorithms are varied, ranging from artificial neural networks and decision trees to random forest and rule-based learners to support vector machines. Therefore, this Special Issue aims to gather the more recent trends and applications of machine learning approaches in tribology.
Prof. Dr. Adolfo Senatore
Prof. Enrico Ciulli
Prof. Dr. Giuseppe Carbone
Guest Editors
Manuscript Submission Information
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Keywords
- tribology
- machine learning
- artificial neural networks
- analysis
- prediction
- optimization
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