Use of catalog mining to extract valuable new knowledge hidden in trivial parameters

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationSystems and Design
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9780791856413
DOIs
Publication statusPublished - Jan 1 2013
Externally publishedYes
EventASME 2013 International Mechanical Engineering Congress and Exposition, IMECE 2013 - San Diego, CA, United States
Duration: Nov 15 2013Nov 21 2013

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume12

Other

OtherASME 2013 International Mechanical Engineering Congress and Exposition, IMECE 2013
CountryUnited States
CitySan Diego, CA
Period11/15/1311/21/13

ASJC Scopus subject areas

  • Mechanical Engineering

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  • Cite this

    Kodama, H., Hirogaki, T., Aoyama, E., & Ogawa, K. (2013). Use of catalog mining to extract valuable new knowledge hidden in trivial parameters. In Systems and Design (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 12). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE2013-63371