Cutting condition decision methodology based on data-mining of tool catalog data

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

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

3 Citations (Scopus)

Abstract

Data-mining methods were used to support decisions about reasonable cutting conditions. The aim of our research was to extract new knowledge by applying data-mining techniques to a tool catalog. We used both hierarchical and non-hierarchical clustering of catalog data and also used applied multiple regression analysis. We focused on the shape element of catalog data and we visually grouped end mills from the viewpoint of tool shape, which here meant the ratio of dimensions, by using the k-means method. We then decreased the number of variables by using hierarchical cluster analysis. We also found an expression for calculating the best cutting conditions, and we compared the calculated values with the catalog values. We did 15 minutes of cutting work using three kinds of cutting conditions: conditions recommended in the catalog, conditions derived by data-mining, and proven cutting conditions for die machining (rough processing).

Original languageEnglish
Title of host publicationASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010
Pages491-499
Number of pages9
Volume2
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010 - Erie, PA, United States
Duration: Oct 12 2010Oct 15 2010

Other

OtherASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010
CountryUnited States
CityErie, PA
Period10/12/1010/15/10

Fingerprint

Data mining
Cluster analysis
Regression analysis
Machining
Processing

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Kodama, H., Hirogaki, T., Aoyama, E., & Ogawa, K. (2010). Cutting condition decision methodology based on data-mining of tool catalog data. In ASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010 (Vol. 2, pp. 491-499) https://doi.org/10.1115/MSEC2010-34199

Cutting condition decision methodology based on data-mining of tool catalog data. / Kodama, Hiroyuki; Hirogaki, Toshiki; Aoyama, Eiichi; Ogawa, Keiji.

ASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010. Vol. 2 2010. p. 491-499.

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

Kodama, H, Hirogaki, T, Aoyama, E & Ogawa, K 2010, Cutting condition decision methodology based on data-mining of tool catalog data. in ASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010. vol. 2, pp. 491-499, ASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010, Erie, PA, United States, 10/12/10. https://doi.org/10.1115/MSEC2010-34199
Kodama H, Hirogaki T, Aoyama E, Ogawa K. Cutting condition decision methodology based on data-mining of tool catalog data. In ASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010. Vol. 2. 2010. p. 491-499 https://doi.org/10.1115/MSEC2010-34199
Kodama, Hiroyuki ; Hirogaki, Toshiki ; Aoyama, Eiichi ; Ogawa, Keiji. / Cutting condition decision methodology based on data-mining of tool catalog data. ASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010. Vol. 2 2010. pp. 491-499
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