Experimental verification of end-milling condition decision support system using data-mining for difficult-to-cut materials

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa, Koichi Okuda

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

4 Citations (Scopus)

Abstract

Data-mining methods using hierarchical and non-hierarchical clustering are proposed, which could help manufacturing engineers determine guidelines for deciding end-milling conditions. We have constructed a novel system that uses clustering techniques and tool catalog data to support the determination of end-milling conditions for different types of recent difficult-to-cut materials. In the present report, we especially focus on the cutting speed to estimate the performance of this system. A comparison with the conditions recommended by famous tool makers in Japan, reveals that our proposed system can be used to determine the cutting speeds for various difficult-to-cut materials. That is, 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; JIS SUS310) showed that the catalog mining method is effective for deriving guidelines for deciding end-milling conditions at the beginning of the manufacturing stage.

Original languageEnglish
Title of host publicationAdvances in Abrasive Technology XVII
EditorsJiwang Yan, Hideki Aoyama, Akinori Yui
PublisherTrans Tech Publications Ltd
Pages334-339
Number of pages6
ISBN (Electronic)9783038352211
DOIs
Publication statusPublished - Jan 1 2014
Externally publishedYes
Event17th International Symposium on Advances in Abrasive Technology, ISAAT 2014 - Kailua, United States
Duration: Sep 22 2014Sep 25 2014

Publication series

NameAdvanced Materials Research
Volume1017
ISSN (Print)1022-6680
ISSN (Electronic)1662-8985

Other

Other17th International Symposium on Advances in Abrasive Technology, ISAAT 2014
CountryUnited States
CityKailua
Period9/22/149/25/14

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Kodama, H., Hirogaki, T., Aoyama, E., Ogawa, K., & Okuda, K. (2014). Experimental verification of end-milling condition decision support system using data-mining for difficult-to-cut materials. In J. Yan, H. Aoyama, & A. Yui (Eds.), Advances in Abrasive Technology XVII (pp. 334-339). (Advanced Materials Research; Vol. 1017). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMR.1017.334