Investigation of End-Milling Condition Decision Methodology Based on Data Mining for Tool Catalog Database
Hiroyuki Kodama*, Toshiki Hirogaki**, Eiichi Aoyama**,
and Keiji Ogawa***
*Graduate School of Engineering, Doshisha University, 1-3 Tataramiyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan
**Faculty of Science and Engineering, Doshisha University, 1-3 Tataramiyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan
***Department of Mechanical Systems Engineering, The University of Shiga Prefecture, 2500 Hassaka-cho, Hikone-shi, Shiga 522-8533, Japan
Data mining supports decision making about reasonable end-milling conditions. Our research objective is to excavate new knowledge with mining effect by applying data mining techniques to a tool catalog. We use hierarchical and nonhierarchical clustering data mining with catalog data by applying multiple regression analysis and focusing on the catalog data shape element. We visually grouped end-mills on the basis of tool shape, considering the ratio of tool shape dimensions, by employing the K-means method. We found that factors related to blade length and full length ratio are effective in for making end-milling condition decisions. These factors have not previously been singled out through background knowledge or expert knowledge, but they were noticed as a data mining effect.
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