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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999781/ https://www.ncbi.nlm.nih.gov/pubmed/35407692 http://dx.doi.org/10.3390/ma15072358 |
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author | Sieberg, Philipp Maximilian Kurtulan, Dzhem Hanke, Stefanie |
author_facet | Sieberg, Philipp Maximilian Kurtulan, Dzhem Hanke, Stefanie |
author_sort | Sieberg, Philipp Maximilian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8999781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89997812022-04-12 Wear Mechanism Classification Using Artificial Intelligence Sieberg, Philipp Maximilian Kurtulan, Dzhem Hanke, Stefanie Materials (Basel) Article 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. MDPI 2022-03-22 /pmc/articles/PMC8999781/ /pubmed/35407692 http://dx.doi.org/10.3390/ma15072358 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sieberg, Philipp Maximilian Kurtulan, Dzhem Hanke, Stefanie Wear Mechanism Classification Using Artificial Intelligence |
title | Wear Mechanism Classification Using Artificial Intelligence |
title_full | Wear Mechanism Classification Using Artificial Intelligence |
title_fullStr | Wear Mechanism Classification Using Artificial Intelligence |
title_full_unstemmed | Wear Mechanism Classification Using Artificial Intelligence |
title_short | Wear Mechanism Classification Using Artificial Intelligence |
title_sort | wear mechanism classification using artificial intelligence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999781/ https://www.ncbi.nlm.nih.gov/pubmed/35407692 http://dx.doi.org/10.3390/ma15072358 |
work_keys_str_mv | AT siebergphilippmaximilian wearmechanismclassificationusingartificialintelligence AT kurtulandzhem wearmechanismclassificationusingartificialintelligence AT hankestefanie wearmechanismclassificationusingartificialintelligence |