Application of machine learning to reduce casting defects from bentonite sand mixtureBreznikar, Žiga (Avtor) Bojinović, Marko (Avtor) Brezočnik, Miran (Avtor) gravity castingmachine learningdefectsclassifierdata scienceOne 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 castingDAAAM International Vienna20242025-03-11 08:56:59Članek v reviji92009sl