Improvement accuracy of cutting condition decision formula using catalog mining

Hiroaki Fukasawa; Hiroyuki Kodama; Toshiki Hirogaki; Eiichi Aoyama; Keiji Ogawa

Improvement accuracy of cutting condition decision formula using catalog mining

Hiroaki Fukasawa, Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

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

Abstract

The aim of our research was to extract new knowledge by applying data mining techniques to machine tool maker catalogs. We cut a SKD61 under three types of cutting conditions: those recommended in tool maker catalogs, those derived from data mining, and those recommended by veteran engineers. Conditions derived from data mining were found to be more stable than those recommended in tool maker catalogs. We fed back based on the catalog mining process. We found improvement accuracy of cutting conditions by calculating the corrective coefficient. As a result, these cutting conditions decision formulas were found to be highly accurate.

Original language	English
Publication status	Published - 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

Cutting tool catalog
Data mining
End mill
Multiple regression analysis

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Cite this

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title = "Improvement accuracy of cutting condition decision formula using catalog mining",

abstract = "The aim of our research was to extract new knowledge by applying data mining techniques to machine tool maker catalogs. We cut a SKD61 under three types of cutting conditions: those recommended in tool maker catalogs, those derived from data mining, and those recommended by veteran engineers. Conditions derived from data mining were found to be more stable than those recommended in tool maker catalogs. We fed back based on the catalog mining process. We found improvement accuracy of cutting conditions by calculating the corrective coefficient. As a result, these cutting conditions decision formulas were found to be highly accurate.",

keywords = "Cutting tool catalog, Data mining, End mill, Multiple regression analysis",

author = "Hiroaki Fukasawa and Hiroyuki Kodama and Toshiki Hirogaki and Eiichi Aoyama and Keiji Ogawa",

year = "2011",

language = "English",

note = "6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011 ; Conference date: 08-11-2011 Through 10-11-2011",

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TY - CONF

T1 - Improvement accuracy of cutting condition decision formula using catalog mining

AU - Fukasawa, Hiroaki

AU - Kodama, Hiroyuki

AU - Hirogaki, Toshiki

AU - Aoyama, Eiichi

AU - Ogawa, Keiji

PY - 2011

Y1 - 2011

N2 - The aim of our research was to extract new knowledge by applying data mining techniques to machine tool maker catalogs. We cut a SKD61 under three types of cutting conditions: those recommended in tool maker catalogs, those derived from data mining, and those recommended by veteran engineers. Conditions derived from data mining were found to be more stable than those recommended in tool maker catalogs. We fed back based on the catalog mining process. We found improvement accuracy of cutting conditions by calculating the corrective coefficient. As a result, these cutting conditions decision formulas were found to be highly accurate.

AB - The aim of our research was to extract new knowledge by applying data mining techniques to machine tool maker catalogs. We cut a SKD61 under three types of cutting conditions: those recommended in tool maker catalogs, those derived from data mining, and those recommended by veteran engineers. Conditions derived from data mining were found to be more stable than those recommended in tool maker catalogs. We fed back based on the catalog mining process. We found improvement accuracy of cutting conditions by calculating the corrective coefficient. As a result, these cutting conditions decision formulas were found to be highly accurate.

KW - Cutting tool catalog

KW - Data mining

KW - End mill

KW - Multiple regression analysis

UR - http://www.scopus.com/inward/record.url?scp=84883363639&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883363639&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:84883363639

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

Y2 - 8 November 2011 through 10 November 2011

ER -

Improvement accuracy of cutting condition decision formula using catalog mining

Abstract

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Keywords

ASJC Scopus subject areas

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