Reprint

Recent Advances in Machine Learning in Tribology

Edited by
July 2024
220 pages
  • ISBN978-3-7258-1737-5 (Hardback)
  • ISBN978-3-7258-1738-2 (PDF)

This book is a reprint of the Special Issue Recent Advances in 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 of research, and its understanding provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and 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 our existing knowledge of this field. Thereby, machine learning (ML) and artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient manner or even real-time way. The first edition of the Special Issue “Machine Learning in Tribology” has already demonstrated the variety of potential applications of these methods, moving beyond purely academic purposes to also encompass industrial applications. This second edition of this Special Issue, entitled “Recent Advances in Machine Learning in Tribology”, covers the latest developments from academic and industrial researchers linked to innovations in the broad field of tribology by employing machine learning and artificial intelligence approaches.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
generative adversarial network (GAN); bearing fault diagnosis; data augmentation; loss function modification; GAN structure improvement; GAN review; thrust bearing; hydrodynamic lubrication; numerical analysis; machine-learning; polynomial regression; SVM; regression trees; machine learning; friction; lubrication; nanoparticles; tribology; artificial neural network; Bayesian optimization; mid-infrared spectra; lubricating oil; LDA-SVM; Kennard–Stone algorithm; condition monitoring; rolling bearing; feature engineering; damage early detection; electrical impedance measurement; convolutional auto-encoder; balanced distribution adaption; domain adaptation; cross-condition fault diagnosis; tool wear; convolutional neural network (CNN); global time feature; informer; BiLSTM; erosion-corrosion wear; ANN; multifactorial analysis; synthetic data; erosion-corrosion data; erosion data; corrosion data; tribology; lubrication; wear particle; ensemble deep learning; convolution neural network; artificial intelligence; machine learning; tribo-informatics; physics-informed neural network; friction; wear; lubrication; machine learning; Gaussian Process Regression; elastohydrodynamic lubrication; elliptical contacts; finite elements; film thickness prediction; supervised learning; regression techniques; surface texturing; dynamic seals; n/a