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”.