Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees
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 tha...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86005-y |
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| author | Dorota Wilk-Kołodziejczyk Aleksandra Nowotny Izabela Krzak Adam Tchórz Krzysztof Jaśkowiec Marcin Małysza Adam Bitka Mirosław Głowacki Marzanna Książek Łukasz Marcjan |
| author_facet | Dorota Wilk-Kołodziejczyk Aleksandra Nowotny Izabela Krzak Adam Tchórz Krzysztof Jaśkowiec Marcin Małysza Adam Bitka Mirosław Głowacki Marzanna Książek Łukasz Marcjan |
| author_sort | Dorota Wilk-Kołodziejczyk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8152d2569ecc43708846ce74acaa0a45 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8152d2569ecc43708846ce74acaa0a452025-01-19T12:23:55ZengNature PortfolioScientific Reports2045-23222025-01-011511810.1038/s41598-025-86005-yDevelopment of a model for detection and analysis of inclusions in tomographic images of iron castings using decision treesDorota Wilk-Kołodziejczyk0Aleksandra Nowotny1Izabela Krzak2Adam Tchórz3Krzysztof Jaśkowiec4Marcin Małysza5Adam Bitka6Mirosław Głowacki7Marzanna Książek8Łukasz Marcjan9Department of Applied Computer Science and Modelling Department, Faculty of Metals Engineering and Industrial Computer Science, AGH University of KrakowDepartment of Applied Computer Science and Modelling Department, Faculty of Metals Engineering and Industrial Computer Science, AGH University of KrakowŁukasiewicz Research Network -Krakow Institute of technologyŁukasiewicz Research Network -Krakow Institute of technologyDepartment of Applied Computer Science and Modelling Department, Faculty of Metals Engineering and Industrial Computer Science, AGH University of KrakowŁukasiewicz Research Network -Krakow Institute of technologyDepartment of Applied Computer Science and Modelling Department, Faculty of Metals Engineering and Industrial Computer Science, AGH University of KrakowDepartment of Applied Computer Science and Modelling Department, Faculty of Metals Engineering and Industrial Computer Science, AGH University of KrakowDepartment of Applied Computer Science and Modelling Department, Faculty of Metals Engineering and Industrial Computer Science, AGH University of KrakowDepartment of Applied Computer Science and Modelling Department, Faculty of Metals Engineering and Industrial Computer Science, AGH University of KrakowAbstract 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.https://doi.org/10.1038/s41598-025-86005-y |
| spellingShingle | Dorota Wilk-Kołodziejczyk Aleksandra Nowotny Izabela Krzak Adam Tchórz Krzysztof Jaśkowiec Marcin Małysza Adam Bitka Mirosław Głowacki Marzanna Książek Łukasz Marcjan Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees Scientific Reports |
| title | Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees |
| title_full | Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees |
| title_fullStr | Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees |
| title_full_unstemmed | Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees |
| title_short | Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees |
| title_sort | development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees |
| url | https://doi.org/10.1038/s41598-025-86005-y |
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