Investigation of end-milling condition decision methodology based on data mining for tool catalog database

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

研究成果査読

8 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)61-74
ページ数14
ジャーナルInternational Journal of Automation Technology
6
1
DOI
出版ステータスPublished - 1月 2012
外部発表はい

ASJC Scopus subject areas

  • 機械工学
  • 産業および生産工学

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