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LCA Estimation of End-Milling Condition Derived From Catalog-Mining Considering Human Learning Curve

[+] Author Affiliations
Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Junichi Sakamoto

Doshisha University, Kyotanabe, Kyoto, Japan

Keiji Ogawa

The University of Shiga Prefecture, Shiga, Japan

Paper No. DETC2012-70843, pp. 1163-1172; 10 pages
doi:10.1115/DETC2012-70843
From:
  • ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 32nd Computers and Information in Engineering Conference, Parts A and B
  • Chicago, Illinois, USA, August 12–15, 2012
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4501-1
  • Copyright © 2012 by ASME

abstract

Choosing cutting tools and end-milling conditions depends on expert engineers’ knowledge and experience, and often a lengthy process of trial and error is required before they obtain appropriate cutting 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 recommended conditions, conditions derived from data-mining (mined conditions), and expert engineer conditions. We used LCA to evaluate quantitatively the environmental impact resulting from these conditions. We designed an index model of the environmental burden in the technical mastering process under the three condition. The results show that unskilled engineers could decrease the cumulative environmental burden by working under the mined condition in the initial stage. Recommending the use of the mined condition in the initial stage is therefore considered best.

Copyright © 2012 by ASME
Topics: Mining , Milling

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