Catalog Mining Using MIC (Maximal Information Coefficient) of Radius End Mill Tool

Abstract

A novel datamining method has been needed because the IoT (Internet of things) is spread over various kinds of industrial fields. We therefore propose to apply a data mining method to tool catalog data-base to improve the manufacturing technologies with machine tools because it is considered to include a useful information derived from tool manufacturing technology as a big-data. In the present report, we look at MIC (Maximal Information Coefficient) as a novel processing method to search for a new knowledge in tool data data-base, and construct a hierarchical clustering method based on MIC as a data mining method. Comparing a predicting equation derived from the conventional catalog mining method based on a traditional statistics with one based on MIC processing method, we investigate a function of MIC in data mining for end milling conditions. As a result, it can be seen that a constructed method (MIC data mining method) makes it feasible efficiently to find out essential variables in the radius end mill database because a derived practical formula has less interactions than conventional one with keeping the same prediction accuracy.