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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Vydáno v:Materials Ročník 15; číslo 7; s. 2358
Hlavní autoři: Sieberg, Philipp Maximilian, Kurtulan, Dzhem, Hanke, Stefanie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 22.03.2022
MDPI
Témata:
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
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Shrnutí: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 analytical techniques are performed in order to analyze wear appearances, i.e., grooves, pittings, surface films, and others. In addition, experience is required in order to draw the correct and relevant conclusions on the acting damage and wear mechanisms from the obtained analytical data. Until now, different types of wear mechanisms are classified by experts examining the damage patterns manually. In addition to this approach based on expert knowledge, the use of artificial intelligence (AI) represents a promising alternative. Here, no expert knowledge is required, instead, the classification is done by a purely data-driven model. In this contribution, artificial neural networks are used to classify the wear mechanisms based on SEM images. In order to obtain optimal performance of the artificial neural network, a hyperparameter optimization is performed in addition. The content of this contribution is the investigation of the feasibility of an AI-based model for the automated classification of wear mechanisms.
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ISSN:1996-1944
1996-1944
DOI:10.3390/ma15072358