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

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 conference › Paper › peer-review

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 language	English
Publication status	Published - Dec 1 2011
Externally published	Yes
Event	6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011 - Omiya Sonic City, Saitama, Japan Duration: Nov 8 2011 → Nov 10 2011

Other

Other	6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011
Country/Territory	Japan
City	Omiya Sonic City, Saitama
Period	11/8/11 → 11/10/11

Keywords

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

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Cite this

Effects on proposed end-milling condition decision-support system using data mining on saving power consumption. / Kodama, Hiroyuki; Hirogaki, Toshiki; Aoyama, Eiichi et al.
2011. Paper presented at 6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011, Omiya Sonic City, Saitama, Japan.

Research output: Contribution to conference › Paper › peer-review

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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.",

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author = "Hiroyuki Kodama and Toshiki Hirogaki and Eiichi Aoyama and Keiji Ogawa and Junichi Sakamoto",

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T1 - Effects on proposed end-milling condition decision-support system using data mining on saving power consumption

AU - Kodama, Hiroyuki

AU - Hirogaki, Toshiki

AU - Aoyama, Eiichi

AU - Ogawa, Keiji

AU - Sakamoto, Junichi

PY - 2011/12/1

Y1 - 2011/12/1

N2 - 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.

AB - 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.

KW - Data mining

KW - End-milling

KW - Environmental Impact

KW - LCA

KW - Learning curve

KW - Manufacturing system

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M3 - Paper

AN - SCOPUS:84883364161

T2 - 6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011

Y2 - 8 November 2011 through 10 November 2011

ER -

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

Abstract

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Keywords

ASJC Scopus subject areas

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