Aid of end-milling condition decision using data mining from tool catalog data for rough processing

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).

Original languageEnglish
Title of host publicationAdvances in Abrasive Technology XIV
Pages345-350
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event14th International Symposium on Advances in Abrasive Technology, ISAAT 2011 - Stuttgart, Germany
Duration: Sep 18 2011 → Sep 21 2011

Publication series

NameAdvanced Materials Research
Volume325
ISSN (Print)1022-6680

Other

Other14th International Symposium on Advances in Abrasive Technology, ISAAT 2011
CountryGermany
CityStuttgart
Period9/18/11 → 9/21/11

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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