20.500.12556/DKUM-92009 Application of machine learning to reduce casting defects from bentonite sand mixture 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 gravity casting machine learning defects classifier data science gravitacijsko litje strojno učenje napake klasifikator znanost o podatkih true false true DAAAM International Vienna Angleški jezik Slovenski jezik https://www.ijsimm.com/ Članek v reviji 2025-03-11 08:56:59 2025-03-11 08:56:59 2025-03-12 03:12:42 0000-00-00 00:00:00 2024 0 0 str. 634-643 no. 4 Vol. 23 Dec. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2024-12-01 004.85:621.74 220112899 10.2507/IJSIMM23-4-702 1726-4529 text23-4_702.pdf text23-4_702.pdf 1 4F8CA049C9331A774EE1AE008CDCB4DE 229aad1901db13b01a3264d8e01a4ba53b910f3cc52fcfd6a5bcdb1b857bbd9e 8f28573b-fe4e-11ef-b15e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189153 https://www.ijsimm.com/Full_Papers/Fulltext2024/text23-4_702.pdf 0 97cf4dd0-fe4e-11ef-b15e-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189154 Fakulteta za strojništvo 0 0 0