Szczegóły publikacji
Opis bibliograficzny
Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees / Dorota WILK-KOŁODZIEJCZYK, Aleksandra Nowotny, Izabela Krzak, Adam Tchórz, Krzysztof JAŚKOWIEC, Marcin Małysza, Adam BITKA, Mirosław GŁOWACKI, Marzanna KSIĄŻEK, Łukasz MARCJAN // Scientific Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2045-2322. — 2025 — vol. 15 art. no. 1880, s. 1-8. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 8, Abstr. — Publikacja dostępna online od: 2025-01-13. — M. Małysza - afiliacja: Łukasiewicz Research Network - Krakow Institute of Technology ; Dod. afiliacja: D. Wilk-Kołodziejczyk, K. Jaśkowiec - Łukasiewicz Research Network - Krakow Institute of Technology ; A. Bitka - Faculty of Foundry Engineering, AGH University of Krakow ; M. Głowacki - Faculty of Natural Sciences, Jan Kochanowski University of Kielce ; M. Książek - Faculty of Non-Ferrous Metals, AGH University of Krakow, Krakow, Poland
Autorzy (10)
- AGHWilk-Kołodziejczyk Dorota
- AGHNowotny Aleksandra
- Krzak Izabela
- Tchórz Adam
- AGHJaśkowiec Krzysztof
- Małysza Marcin
- AGHBitka Adam
- AGHGłowacki Mirosław
- AGHKsiążek Marzanna Maria
- AGHMarcjan Łukasz
Dane bibliometryczne
| ID BaDAP | 157641 |
|---|---|
| Data dodania do BaDAP | 2025-02-13 |
| Tekst źródłowy | URL |
| DOI | 10.1038/s41598-025-86005-y |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Scientific Reports |
Abstract
CT images of castings made of ductile iron were analyzed in the paper. On these images, objects can be identified that can be considered as graphite precipitates or indicate the presence of a defect in the casting. Research conducted in this area is described, based on experimental data that allows to determine whether the indicated components present in the casting are graphite precipitation. Analyzing the results, a conclusion was drawn that the classification based solely on the input data used is insufficient. Such action allowed to obtain information that there are particles in the casting that can be both graphite separation and imperfections (in particular voids, porosities, discontinuities). These results are subjected to further analysis (pictures) to help decide whether the object is a separation or a discontinuity. The available (experimental) data make it possible to unequivocally identify belonging to one of these groups. The use of machine learning methods to recognize the relationships between the physical parameters of particles helps to improve the analysis process. An important aspect was the determination of three ranges in the scale of shades of gray, which were used to determine the labels for the input data. Lighter shades in the first range indicate slight differences in the density of the particle, and thus suggest the occurrence of cast fineness. The middle range corresponding to the darker shades of gray was assigned to particles that could be shrinkage porosities. The darkest shades corresponded to occurrences of gas porosities (voids). Shades of gray cannot be the only determinant of the type of microstructure component, because apart from imperfections, there are also graphite precipitations in the casting (shape and shade of gray resembling emptiness). It cannot be assumed that specific types of defects will occur in the tested object (e.g. only gas porosities), which requires additional analysis of the microstructure image.
