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220822 ||| eng |
| 020 |
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|a 9783036539812
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|a 9783036539829
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|a books978-3-0365-3982-9
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| 100 |
1 |
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|a Tremmel, Stephan
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| 245 |
0 |
0 |
|a Machine Learning in Tribology
|h Elektronische Ressource
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| 260 |
|
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (208 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a unbalanced datasets
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| 653 |
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|a data mining
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| 653 |
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|a random forest
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| 653 |
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|a random forest classifier
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| 653 |
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|a condition monitoring
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| 653 |
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|a rolling bearing dynamics
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| 653 |
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|a Convolutional Neural Network (CNN)
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| 653 |
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|a n/a
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| 653 |
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|a meta-modeling
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| 653 |
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|a fault data generation
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| 653 |
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|a reduced order modelling
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| 653 |
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|a monitoring
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| 653 |
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|a digital twin
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| 653 |
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|a neural networks
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| 653 |
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|a structure-borne sound
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| 653 |
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|a History of engineering & technology / bicssc
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| 653 |
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|a BERT
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a remaining useful life
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| 653 |
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|a tribo-testing
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| 653 |
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|a rubber seal applications
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| 653 |
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|a rolling bearings
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| 653 |
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|a laser surface texturing
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| 653 |
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|a total knee replacement
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| 653 |
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|a tribAIn
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| 653 |
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|a evolutionary algorithms
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| 653 |
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|a gradient boosting
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| 653 |
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|a databases
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| 653 |
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|a dynamic friction
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| 653 |
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|a feature engineering
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| 653 |
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|a reynolds equation
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| 653 |
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|a analysis
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| 653 |
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|a prediction
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| 653 |
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|a triboinformatics
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| 653 |
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|a regression
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| 653 |
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|a self-lubricating journal bearings
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| 653 |
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|a tribo-informatics
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| 653 |
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|a UHWMPE
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| 653 |
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|a PINN
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| 653 |
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|a Generative Adversarial Network (GAN)
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| 653 |
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|a artificial intelligence
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| 653 |
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|a semi-supervised learning
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| 653 |
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|a natural language processing
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| 653 |
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|a Gaussian processes
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| 653 |
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|a artificial neural networks
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| 653 |
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|a tribology
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| 653 |
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|a cage instability
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| 653 |
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|a tensor decomposition
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| 653 |
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|a optimization
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| 653 |
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|a texturing during moulding
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| 653 |
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|a amorphous carbon coatings
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| 653 |
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|a bearing fault diagnosis
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| 700 |
1 |
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|a Marian, Max
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| 700 |
1 |
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|a Tremmel, Stephan
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| 700 |
1 |
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|a Marian, Max
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 024 |
8 |
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|a 10.3390/books978-3-0365-3982-9
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/84499
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/5482
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 400
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| 082 |
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|a 900
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| 082 |
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|a 576
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|a 700
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|a 600
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|a 620
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|a 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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