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IJAT Vol.6 No.1 pp. 61-74
doi: 10.20965/ijat.2012.p0061
(2012)

Paper:

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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

Received:
November 26, 2010
Accepted:
September 13, 2011
Published:
January 5, 2012
Creative Commons License
Keywords:
end-milling condition, catalog data, data mining, K-means method, multiple regression analysis
Abstract
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.
Cite this article as:
H. Kodama, T. Hirogaki, E. Aoyama, and K. Ogawa, “Investigation of End-Milling Condition Decision Methodology Based on Data Mining for Tool Catalog Database,” Int. J. Automation Technol., Vol.6 No.1, pp. 61-74, 2012.
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Last updated on Dec. 02, 2024