Effects on proposed end-milling condition decision-support system using data mining on saving power consumption

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa, Junichi Sakamoto

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Choosing cutting tools and conditions depends on expert engineers' knowledge and experience, and often a lengthy process of trial and error is required before they obtain appropriate end-milling conditions. We have previously proposed data mining methods to make decisions about end-milling conditions on the basis of catalog data. We cut hardened die steel JIS SKD61 under three kinds of end-milling conditions: catalog conditions, mined conditions, expert engineer conditions. We used LCA to quantitatively evaluate the environmental impact resulting from these conditions. Results showed that the mined condition is environmentally superior to the catalog conditions.

Original languageEnglish
Publication statusPublished - Dec 1 2011
Externally publishedYes
Event6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011 - Omiya Sonic City, Saitama, Japan
Duration: Nov 8 2011Nov 10 2011

Other

Other6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011
CountryJapan
CityOmiya Sonic City, Saitama
Period11/8/1111/10/11

Keywords

  • Data mining
  • End-milling
  • Environmental Impact
  • LCA
  • Learning curve
  • Manufacturing system

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Effects on proposed end-milling condition decision-support system using data mining on saving power consumption'. Together they form a unique fingerprint.

Cite this