Szczegóły publikacji
Opis bibliograficzny
Development of a CT image analysis model for cast iron products based on artificial intelligence methods / Adam Tchórz, Krzysztof Korona, Izabela Krzak, Adam Bitka, Marzanna KSIĄŻEK, Krzysztof Jaśkowiec, Marcin Małysza, Mirosław GŁOWACKI, Dorota WILK-KOŁODZIEJCZYK // Materials [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1944. — 2022 — vol. 15 iss. 22 art. no. 8254, s. 1-16. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 16, Abstr. — Publikacja dostępna online od: 2022-11-21. — M. Głowacki - dod. afiliacja: Jan Kochanowski University of Humanities and Sciences in Kielce ; D. Wilk-Kołodziejczyk - dod. afiliacja: Łukasiewicz Research Network–Krakow Institute of Technology ; A. Bitka – afiliacja: Sieć Badawcza Łukasiewicz - Krakowski Instytut Technologiczny
Autorzy (9)
- Tchórz Adam
- AGHKorona Krzysztof
- Krzak Izabela
- Bitka Adam
- AGHKsiążek Marzanna Maria
- Jaśkowiec Krzysztof
- Małysza Marcin
- AGHGłowacki Mirosław
- AGHWilk-Kołodziejczyk Dorota
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 143833 |
|---|---|
| Data dodania do BaDAP | 2022-11-28 |
| Tekst źródłowy | URL |
| DOI | 10.3390/ma15228254 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Materials |
Abstract
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.