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

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)61-74
Number of pages14
JournalInternational Journal of Automation Technology
Volume6
Issue number1
DOIs
Publication statusPublished - Jan 2012
Externally publishedYes

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Keywords

  • Catalog data
  • Data mining
  • End-milling condition
  • K-means method
  • Multiple regression analysis

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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