Machine Learning in Tribology

doi:10.3390/books978-3-0365-3982-9

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Machine Learning in Tribology

Edited by

May 2022

208 pages

ISBN978-3-0365-3981-2 (Hardback)
ISBN978-3-0365-3982-9 (PDF)

https://doi.org/10.3390/books978-3-0365-3982-9

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This book is a reprint of the Special Issue Machine Learning in Tribology that was published in

Chemistry & Materials Science

Engineering

Summary

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

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Keywords

artificial intelligence; machine learning; artificial neural networks; tribology; condition monitoring; semi-supervised learning; random forest classifier; self-lubricating journal bearings; reduced order modelling; dynamic friction; rubber seal applications; tensor decomposition; laser surface texturing; texturing during moulding; digital twin; machine learning; artificial intelligence; PINN; machine learning; reynolds equation; tribology; machine learning; artificial intelligence; triboinformatics; databases; data mining; meta-modeling; artificial neural networks; monitoring; analysis; prediction; optimization; fault data generation; Convolutional Neural Network (CNN); Generative Adversarial Network (GAN); bearing fault diagnosis; unbalanced datasets; tribo-testing; tribo-informatics; machine learning; artificial intelligence; natural language processing; tribAIn; BERT; machine learning; amorphous carbon coatings; UHWMPE; total knee replacement; Gaussian processes; rolling bearing dynamics; cage instability; regression; machine learning; neural networks; random forest; gradient boosting; evolutionary algorithms; rolling bearings; remaining useful life; machine learning; feature engineering; condition monitoring; structure-borne sound; random forest; regression; n/a