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.
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ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 12–15, 2012
Chicago, Illinois, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-4501-1
PROCEEDINGS PAPER
LCA Estimation of End-Milling Condition Derived From Catalog-Mining Considering Human Learning Curve
Hiroyuki Kodama,
Hiroyuki Kodama
Doshisha University, Kyotanabe, Kyoto, Japan
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Toshiki Hirogaki,
Toshiki Hirogaki
Doshisha University, Kyotanabe, Kyoto, Japan
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Eiichi Aoyama,
Eiichi Aoyama
Doshisha University, Kyotanabe, Kyoto, Japan
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Keiji Ogawa,
Keiji Ogawa
The University of Shiga Prefecture, Shiga, Japan
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Junichi Sakamoto
Junichi Sakamoto
Doshisha University, Kyotanabe, Kyoto, Japan
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Hiroyuki Kodama
Doshisha University, Kyotanabe, Kyoto, Japan
Toshiki Hirogaki
Doshisha University, Kyotanabe, Kyoto, Japan
Eiichi Aoyama
Doshisha University, Kyotanabe, Kyoto, Japan
Keiji Ogawa
The University of Shiga Prefecture, Shiga, Japan
Junichi Sakamoto
Doshisha University, Kyotanabe, Kyoto, Japan
Paper No:
DETC2012-70843, pp. 1163-1172; 10 pages
Published Online:
September 9, 2013
Citation
Kodama, H, Hirogaki, T, Aoyama, E, Ogawa, K, & Sakamoto, J. "LCA Estimation of End-Milling Condition Derived From Catalog-Mining Considering Human Learning Curve." Proceedings of the 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. pp. 1163-1172. ASME. https://doi.org/10.1115/DETC2012-70843
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