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Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods
This paper presents an assessment of the possibility of using digital image classifiers for tomographic images concerning ductile iron castings. The results of this work can help the development of an efficient system suggestion allowing for decision making regarding the qualitative assessment of th...
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/PMC9694151/ https://www.ncbi.nlm.nih.gov/pubmed/36431739 http://dx.doi.org/10.3390/ma15228254 |
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author | Tchórz, Adam Korona, Krzysztof Krzak, Izabela Bitka, Adam Książek, Marzanna Jaśkowiec, Krzysztof Małysza, Marcin Głowacki, Mirosław Wilk-Kołodziejczyk, Dorota |
author_facet | Tchórz, Adam Korona, Krzysztof Krzak, Izabela Bitka, Adam Książek, Marzanna Jaśkowiec, Krzysztof Małysza, Marcin Głowacki, Mirosław Wilk-Kołodziejczyk, Dorota |
author_sort | Tchórz, Adam |
collection | PubMed |
description | This paper presents an assessment of the possibility of using digital image classifiers for tomographic images concerning ductile iron castings. The results of this work can help the development of an efficient system suggestion allowing for decision making regarding the qualitative assessment of the casting process parameters. Special attention should be focused on the fact that automatic classification in the case of ductile iron castings is difficult to perform. The biggest problem in this aspect is the high similarity of the void image, which may be a sign of a defect, and the nodular graphite image. Depending on the parameters, the tests on different photos may look similar. Presented in this article are test scenarios of the module analyzing two-dimensional tomographic images focused on the comprehensive assessment by convolutional neural network models, which are designed to classify the provided image. For the purposes of the tests, three such models were created, different from each other in terms of architecture and the number of hyperparameters and trainable parameters. The described study is a part of the decision-making system, supporting the process of qualitative analysis of the obtained cast iron castings. |
format | Online Article Text |
id | pubmed-9694151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96941512022-11-26 Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods Tchórz, Adam Korona, Krzysztof Krzak, Izabela Bitka, Adam Książek, Marzanna Jaśkowiec, Krzysztof Małysza, Marcin Głowacki, Mirosław Wilk-Kołodziejczyk, Dorota Materials (Basel) Article This paper presents an assessment of the possibility of using digital image classifiers for tomographic images concerning ductile iron castings. The results of this work can help the development of an efficient system suggestion allowing for decision making regarding the qualitative assessment of the casting process parameters. Special attention should be focused on the fact that automatic classification in the case of ductile iron castings is difficult to perform. The biggest problem in this aspect is the high similarity of the void image, which may be a sign of a defect, and the nodular graphite image. Depending on the parameters, the tests on different photos may look similar. Presented in this article are test scenarios of the module analyzing two-dimensional tomographic images focused on the comprehensive assessment by convolutional neural network models, which are designed to classify the provided image. For the purposes of the tests, three such models were created, different from each other in terms of architecture and the number of hyperparameters and trainable parameters. The described study is a part of the decision-making system, supporting the process of qualitative analysis of the obtained cast iron castings. MDPI 2022-11-21 /pmc/articles/PMC9694151/ /pubmed/36431739 http://dx.doi.org/10.3390/ma15228254 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 Tchórz, Adam Korona, Krzysztof Krzak, Izabela Bitka, Adam Książek, Marzanna Jaśkowiec, Krzysztof Małysza, Marcin Głowacki, Mirosław Wilk-Kołodziejczyk, Dorota Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods |
title | Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods |
title_full | Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods |
title_fullStr | Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods |
title_full_unstemmed | Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods |
title_short | Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods |
title_sort | development of a ct image analysis model for cast iron products based on artificial intelligence methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694151/ https://www.ncbi.nlm.nih.gov/pubmed/36431739 http://dx.doi.org/10.3390/ma15228254 |
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