Improvement accuracy of cutting condition decision formula using catalog mining

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

Research output: Contribution to conferencePaperpeer-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 languageEnglish
Publication statusPublished - Dec 1 2011
Externally publishedYes
Event6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011 - Omiya Sonic City, Saitama, Japan
Duration: Nov 8 2011Nov 10 2011

Other

Other6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011
CountryJapan
CityOmiya Sonic City, Saitama
Period11/8/1111/10/11

Keywords

  • Cutting tool catalog
  • Data mining
  • End mill
  • Multiple regression analysis

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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