Abstract
Data mining methods were used to support decisions regarding reasonable cutting conditions. The aim of our research was to extract new knowledge by applying data mining techniques to a tool catalog. We used both hierarchical and non-hierarchical clustering of catalog data and also used applied multiple regression analysis. We focused on the shape element of catalog data and visually grouped end mills from the viewpoint of tool shape, which here meant the ratio of dimensions, using the k-means method. We then decreased the number of variables using hierarchical cluster analysis. We also found an expression for calculating the best cutting conditions, and we compared the calculated values with the catalog values.
