TY - GEN
T1 - Use of catalog mining to extract valuable new knowledge hidden in trivial parameters
AU - Kodama, Hiroyuki
AU - Hirogaki, Toshiki
AU - Aoyama, Eiichi
AU - Ogawa, Keiji
PY - 2013/1/1
Y1 - 2013/1/1
N2 - We have developed a process that uses both hierarchical and non-hierarchical clustering methods to mine data in tool catalogs. Principal component regression is used for quantifying the correlation between the predictor and criterion variables, and multiple regression analysis is used for creating an end-milling condition determinant matrix for each cluster. We fixed the outside diameter of the tool shape parameter as a constant trivial value and examined the correlation between the other tool shape parameters and the end-milling conditions. We thereby extracted valuable new knowledge hidden in trivial parameters and built a hypothesis in regards to data-mining effect. We found that cutting speed is the most important of the criterion variables and that the number of determination coefficient is no less important for determining prediction accuracy of end-milling condition decision equations. Endmilling condition decision determinants derived from our datamining process are important indicators for adjusting endmilling conditions on the basis of end-milling efficiency and tool life.
AB - We have developed a process that uses both hierarchical and non-hierarchical clustering methods to mine data in tool catalogs. Principal component regression is used for quantifying the correlation between the predictor and criterion variables, and multiple regression analysis is used for creating an end-milling condition determinant matrix for each cluster. We fixed the outside diameter of the tool shape parameter as a constant trivial value and examined the correlation between the other tool shape parameters and the end-milling conditions. We thereby extracted valuable new knowledge hidden in trivial parameters and built a hypothesis in regards to data-mining effect. We found that cutting speed is the most important of the criterion variables and that the number of determination coefficient is no less important for determining prediction accuracy of end-milling condition decision equations. Endmilling condition decision determinants derived from our datamining process are important indicators for adjusting endmilling conditions on the basis of end-milling efficiency and tool life.
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U2 - 10.1115/IMECE2013-63371
DO - 10.1115/IMECE2013-63371
M3 - Conference contribution
AN - SCOPUS:84903451181
SN - 9780791856413
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Systems and Design
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2013 International Mechanical Engineering Congress and Exposition, IMECE 2013
Y2 - 15 November 2013 through 21 November 2013
ER -