An indicative end-milling condition decision support system using data-mining for difficult-to-cut materials based on comparison with irregular pitch and lead end-mill and general purpose end-mill

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

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

1 Citation (Scopus)

Abstract

Data-mining methods using hierarchical and non-hierarchical clustering are proposed that will help engineers determine appropriate end-milling conditions. We have constructed a system that uses clustering techniques and tool catalog data to support the determination of end-milling conditions for different types of difficult-to-cut materials such as austenitic stainless steel, Ni-base superalloy, and titanium alloy. Variable cluster analysis and the K-means method were used together to identify tool shape parameters that have a linear relationship with the end-milling conditions listed in the catalogs. The response surface method and significant tool shape parameters obtained by clustering were used to derive end-milling condition decision equations, which were used to determine the indicative end-milling conditions for each material. Comparison with the conditions recommended by toolmakers demonstrated that our proposed system can be used to determine the cutting speeds for various difficult-to-cut materials.

Original languageEnglish
Title of host publicationAdvances in Abrasive Technology XVI
Pages177-182
Number of pages6
Volume797
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event16th International Symposium on Advances in Abrasive Technology, ISAAT 2013 and 17th Chinese Conference of Abrasive Technology, CCAT 2013 - Hangzhou, China
Duration: Sep 23 2013Sep 26 2013

Publication series

NameAdvanced Materials Research
Volume797
ISSN (Print)1022-6680

Other

Other16th International Symposium on Advances in Abrasive Technology, ISAAT 2013 and 17th Chinese Conference of Abrasive Technology, CCAT 2013
CountryChina
CityHangzhou
Period9/23/139/26/13

Fingerprint

Decision support systems
Data mining
Lead
Cluster analysis
Austenitic stainless steel
Superalloys
Titanium alloys
Engineers

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)

Cite this

An indicative end-milling condition decision support system using data-mining for difficult-to-cut materials based on comparison with irregular pitch and lead end-mill and general purpose end-mill. / Kodama, Hiroyuki; Hirogaki, Toshiki; Aoyama, Eiichi; Ogawa, Keiji.

Advances in Abrasive Technology XVI. Vol. 797 2013. p. 177-182 (Advanced Materials Research; Vol. 797).

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

Kodama, H, Hirogaki, T, Aoyama, E & Ogawa, K 2013, An indicative end-milling condition decision support system using data-mining for difficult-to-cut materials based on comparison with irregular pitch and lead end-mill and general purpose end-mill. in Advances in Abrasive Technology XVI. vol. 797, Advanced Materials Research, vol. 797, pp. 177-182, 16th International Symposium on Advances in Abrasive Technology, ISAAT 2013 and 17th Chinese Conference of Abrasive Technology, CCAT 2013, Hangzhou, China, 9/23/13. https://doi.org/10.4028/www.scientific.net/AMR.797.177
Kodama, Hiroyuki ; Hirogaki, Toshiki ; Aoyama, Eiichi ; Ogawa, Keiji. / An indicative end-milling condition decision support system using data-mining for difficult-to-cut materials based on comparison with irregular pitch and lead end-mill and general purpose end-mill. Advances in Abrasive Technology XVI. Vol. 797 2013. pp. 177-182 (Advanced Materials Research).
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