| Title: | Application of machine learning to reduce casting defects from bentonite sand mixture |
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| Authors: | ID Breznikar, Žiga (Author)
ID Bojinović, Marko (Author)
ID Brezočnik, Miran (Author) |
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| Files: |  text23-4_702.pdf (518,07 KB)
MD5: 4F8CA049C9331A774EE1AE008CDCB4DE
 https://www.ijsimm.com/Full_Papers/Fulltext2024/text23-4_702.pdf
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | One of the largest Slovenian foundries (referred to as Company X) primarily focuses on casting moulds for the glass industry. In collaboration with Pro Labor d.o.o., Company X has been systematically gathering defect data since 2021. The analysis revealed that the majority of scrap caused by technological issues is attributed to sand defects. The initial dataset included information on defect occurrences, technological parameters of sand mixture and chemical properties of the cast material. This raw data was refined using data science techniques and statistical methods to support classification. Multiple binary classification models were developed, using sand mixture parameters as inputs, to distinguish between good casting and scrap, with the k-nearest neighbours algorithm. Their performances were evaluated using various classification metrics. Additionally, recommendations were made for development of a real-time industrial application to optimize and regulate pouring temperature in the foundry process. This is based on simulating different pouring temperatures while keeping the other parameters fixed, selecting the temperature that maximizes the likelihood of successful casting |
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| Keywords: | gravity casting, machine learning, defects, classifier, data science |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.12.2024 |
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| Publisher: | DAAAM International Vienna |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 634-643 |
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| Numbering: | Vol. 23, no. 4 |
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| PID: | 20.500.12556/DKUM-92009  |
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| UDC: | 004.85:621.74 |
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| ISSN on article: | 1726-4529 |
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| COBISS.SI-ID: | 220112899 |
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| DOI: | 10.2507/IJSIMM23-4-702 |
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| Publication date in DKUM: | 11.03.2025 |
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| Views: | 226 |
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| Downloads: | 22 |
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| Metadata: |    |
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| Categories: | Misc.
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