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 |
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Pages (from-to) | 61-74 |
Number of pages | 14 |
Journal | International Journal of Automation Technology |
Volume | 6 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2012 |
Externally published | Yes |
Keywords
- Catalog data
- Data mining
- End-milling condition
- K-means method
- Multiple regression analysis
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering