Machine Learning in Tribology

• Muut tekijät: Tremmel, Stephan (Toimittaja), Marian, Max (Toimittaja)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Aiheet: Technology: general issues; History of engineering & technology; 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; PINN; reynolds equation; triboinformatics; databases; data mining; meta-modeling; monitoring; analysis; prediction; optimization; fault data generation; Convolutional Neural Network (CNN); Generative Adversarial Network (GAN); bearing fault diagnosis; unbalanced datasets; tribo-testing; tribo-informatics; natural language processing; tribAIn; BERT; amorphous carbon coatings; UHWMPE; total knee replacement; Gaussian processes; rolling bearing dynamics; cage instability; regression; neural networks; random forest; gradient boosting; evolutionary algorithms; rolling bearings; remaining useful life; feature engineering; structure-borne sound; n/a
• Linkit: DOAB: download the publication; DOAB: description of the publication

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 in...