TY - JOUR N2 - The paper presents an example of Instance-Based Learning using a supervised classification method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors’ computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with five most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high efficiency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of fit occurs for specific values of accepted weights (set #5) for number k = 5 nearest neighbours and taking into account the search criterion according to “product index”. L1 - http://journals.pan.pl/Content/114823/PDF/11-389.pdf L2 - http://journals.pan.pl/Content/114823 PY - 2019 IS - No 4 DO - 10.24425/mper.2019.131450 KW - Soft modelling KW - instance-based learning KW - k-nearest neighbours algorithm KW - cast iron castingdefects KW - computer application A1 - Sika, Robert A1 - Szajewski, Damian A1 - Hajkowski, Jakub A1 - Popielarski, Paweł PB - Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management VL - vol. 10 DA - 2019.12.27 T1 - Application of instance-based learning for cast iron casting defects prediction UR - http://journals.pan.pl/dlibra/publication/edition/114823 T2 - Management and Production Engineering Review ER -