Abstract
Data mining supports decision making about reasonable end-milling conditions. Our research objective is to excavate new knowledge with mining effect by applying data mining techniques to a tool catalog. We use hierarchical and nonhierarchical clustering data mining with catalog data by applying multiple regression analysis and focusing on the catalog data shape element. We visually grouped end-mills on the basis of tool shape, considering the ratio of tool shape dimensions, by employing the K-means method. We found that factors related to blade length and full length ratio are effective in for making end-milling condition decisions. These factors have not previously been singled out through background knowledge or expert knowledge, but they were noticed as a data mining effect.
| Original language | English |
|---|---|
| Pages (from-to) | 61-74 |
| Number of pages | 14 |
| Journal | International Journal of Automation Technology |
| Volume | 6 |
| Issue number | 1 |
| Publication status | Published - Jan 2012 |
| Externally published | Yes |
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Keywords
- Catalog data
- Data mining
- End-milling condition
- K-means method
- Multiple regression analysis
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering
Cite this
Investigation of end-milling condition decision methodology based on data mining for tool catalog database. / Kodama, Hiroyuki; Hirogaki, Toshiki; Aoyama, Eiichi; Ogawa, Keiji.
In: International Journal of Automation Technology, Vol. 6, No. 1, 01.2012, p. 61-74.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Investigation of end-milling condition decision methodology based on data mining for tool catalog database
AU - Kodama, Hiroyuki
AU - Hirogaki, Toshiki
AU - Aoyama, Eiichi
AU - Ogawa, Keiji
PY - 2012/1
Y1 - 2012/1
N2 - Data mining supports decision making about reasonable end-milling conditions. Our research objective is to excavate new knowledge with mining effect by applying data mining techniques to a tool catalog. We use hierarchical and nonhierarchical clustering data mining with catalog data by applying multiple regression analysis and focusing on the catalog data shape element. We visually grouped end-mills on the basis of tool shape, considering the ratio of tool shape dimensions, by employing the K-means method. We found that factors related to blade length and full length ratio are effective in for making end-milling condition decisions. These factors have not previously been singled out through background knowledge or expert knowledge, but they were noticed as a data mining effect.
AB - Data mining supports decision making about reasonable end-milling conditions. Our research objective is to excavate new knowledge with mining effect by applying data mining techniques to a tool catalog. We use hierarchical and nonhierarchical clustering data mining with catalog data by applying multiple regression analysis and focusing on the catalog data shape element. We visually grouped end-mills on the basis of tool shape, considering the ratio of tool shape dimensions, by employing the K-means method. We found that factors related to blade length and full length ratio are effective in for making end-milling condition decisions. These factors have not previously been singled out through background knowledge or expert knowledge, but they were noticed as a data mining effect.
KW - Catalog data
KW - Data mining
KW - End-milling condition
KW - K-means method
KW - Multiple regression analysis
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UR - http://www.scopus.com/inward/citedby.url?scp=84856204286&partnerID=8YFLogxK
M3 - Article
VL - 6
SP - 61
EP - 74
JO - International Journal of Automation Technology
JF - International Journal of Automation Technology
SN - 1881-7629
IS - 1
ER -