Wear Mechanism Classification Using Artificial Intelligence

Understanding the acting wear mechanisms in many cases is key to predicting lifetime, developing models describing component behavior, or for the improvement of the performance of components under tribological loading. Conventionally scanning electron microscopy (SEM) and sometimes additional analyt...

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Veröffentlicht in:	Materials Jg. 15; H. 7; S. 2358
Hauptverfasser:	Sieberg, Philipp Maximilian, Kurtulan, Dzhem, Hanke, Stefanie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 22.03.2022 MDPI
Schlagworte:	Artificial intelligence Artificial neural networks Classification Crack propagation Damage patterns Grooves Image classification Lubricants & lubrication Machine learning Mathematical models Measurement techniques Neural networks Optimization Scanning electron microscopy Service life assessment Tribology Wear mechanisms sliding wear classification wear mechanism artificial intelligence
ISSN:	1996-1944, 1996-1944
Online-Zugang:	Volltext
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