An end-milling condition decision support system using data-mining for difficult-to-cut materials

Hiroyuki Kodama, Masatoshi Shindou, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We proposed the data-mining methods using hierarchical and non-hierarchical clustering methods to help engineers decide appropriate end-milling conditions. The aim of our research is to construct a system that uses clustering techniques and tool catalog data to support the decision of end-milling conditions for difficult-to-cut materials. We used variable cluster analysis and the K-means method to find tool shape parameters that had a linear relationship with the end-milling conditions listed in the catalog. We used the response surface method and significant tool shape parameters obtained by clustering to derive end-milling condition. Milling experiments using a square end mill under two sets of end-milling conditions (conditions derived from the end-milling condition decision support system and conditions suggested by expert engineers) for difficult-to-cut materials (austenite stainless steel) showed that catalog mining can be used to derive guidelines for deciding end-milling conditions.

Original languageEnglish
Title of host publicationAdvances in Abrasive Technology XV
Pages472-477
Number of pages6
DOIs
Publication statusPublished - Nov 26 2012
Externally publishedYes
Event15th International Symposium on Advances in Abrasive Technology, ISAAT 2012 - , Singapore
Duration: Sep 25 2012Sep 28 2012

Publication series

NameAdvanced Materials Research
Volume565
ISSN (Print)1022-6680

Other

Other15th International Symposium on Advances in Abrasive Technology, ISAAT 2012
CountrySingapore
Period9/25/129/28/12

Keywords

  • Catalog data
  • Data mining
  • Difficult-to-cut materials
  • End-milling
  • Hierarchical and non-hierarchical clustering
  • Jis sus310s
  • Response surface method

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Kodama, H., Shindou, M., Hirogaki, T., Aoyama, E., & Ogawa, K. (2012). An end-milling condition decision support system using data-mining for difficult-to-cut materials. In Advances in Abrasive Technology XV (pp. 472-477). (Advanced Materials Research; Vol. 565). https://doi.org/10.4028/www.scientific.net/AMR.565.472