LCA estimation of end-milling condition derived from catalog-mining considering human learning curve

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

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

Abstract

Choosing cutting tools and end-milling conditions depends on expert engineersâTM 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 datamining (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.

Original languageEnglish
Title of host publicationASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012
Pages1163-1172
Number of pages10
EditionPARTS A AND B
DOIs
Publication statusPublished - Dec 1 2012
Externally publishedYes
EventASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012 - Chicago, IL, United States
Duration: Aug 12 2012Aug 12 2012

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
NumberPARTS A AND B
Volume2

Other

OtherASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012
CountryUnited States
CityChicago, IL
Period8/12/128/12/12

ASJC Scopus subject areas

  • Modelling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Kodama, H., Hirogaki, T., Aoyama, E., Ogawa, K., & Sakamoto, J. (2012). LCA estimation of end-milling condition derived from catalog-mining considering human learning curve. In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012 (PARTS A AND B ed., pp. 1163-1172). (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2, No. PARTS A AND B). https://doi.org/10.1115/DETC2012-70843