Abstract
The aim of our research was to extract new knowledge by applying data mining techniques to machine tool maker catalogs. We cut a SKD61 under three types of cutting conditions: those recommended in tool maker catalogs, those derived from data mining, and those recommended by veteran engineers. Conditions derived from data mining were found to be more stable than those recommended in tool maker catalogs. We fed back based on the catalog mining process. We found improvement accuracy of cutting conditions by calculating the corrective coefficient. As a result, these cutting conditions decision formulas were found to be highly accurate.
