TY - GEN
T1 - Aid of end-milling condition decision using data mining from tool catalog data for rough processing
AU - Kodama, Hiroyuki
AU - Hirogaki, Toshiki
AU - Aoyama, Eiichi
AU - Ogawa, Keiji
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).
AB - The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).
KW - Catalog data
KW - Cluster analysis
KW - Data mining
KW - End-milling
KW - K-means method
KW - Multiple regression analysis
UR - http://www.scopus.com/inward/record.url?scp=80052996114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052996114&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMR.325.345
DO - 10.4028/www.scientific.net/AMR.325.345
M3 - Conference contribution
AN - SCOPUS:80052996114
SN - 9783037852316
T3 - Advanced Materials Research
SP - 345
EP - 350
BT - Advances in Abrasive Technology XIV
T2 - 14th International Symposium on Advances in Abrasive Technology, ISAAT 2011
Y2 - 18 September 2011 through 21 September 2011
ER -