Abstract
The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).
| Original language | English |
|---|---|
| Title of host publication | Advances in Abrasive Technology XIV |
| Pages | 345-350 |
| Number of pages | 6 |
| Volume | 325 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 14th International Symposium on Advances in Abrasive Technology, ISAAT 2011 - Stuttgart, Germany Duration: Sep 18 2011 → Sep 21 2011 |
Publication series
| Name | Advanced Materials Research |
|---|---|
| Volume | 325 |
| ISSN (Print) | 1022-6680 |
Other
| Other | 14th International Symposium on Advances in Abrasive Technology, ISAAT 2011 |
|---|---|
| Country | Germany |
| City | Stuttgart |
| Period | 9/18/11 → 9/21/11 |
Fingerprint
Keywords
- Catalog data
- Cluster analysis
- Data mining
- End-milling
- K-means method
- Multiple regression analysis
ASJC Scopus subject areas
- Engineering(all)
Cite this
Aid of end-milling condition decision using data mining from tool catalog data for rough processing. / Kodama, Hiroyuki; Hirogaki, Toshiki; Aoyama, Eiichi; Ogawa, Keiji.
Advances in Abrasive Technology XIV. Vol. 325 2011. p. 345-350 (Advanced Materials Research; Vol. 325).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Aid of end-milling condition decision using data mining from tool catalog data for rough processing
AU - Kodama, Hiroyuki
AU - Hirogaki, Toshiki
AU - Aoyama, Eiichi
AU - Ogawa, Keiji
PY - 2011
Y1 - 2011
N2 - The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).
AB - The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).
KW - Catalog data
KW - Cluster analysis
KW - Data mining
KW - End-milling
KW - K-means method
KW - Multiple regression analysis
UR - http://www.scopus.com/inward/record.url?scp=80052996114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052996114&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMR.325.345
DO - 10.4028/www.scientific.net/AMR.325.345
M3 - Conference contribution
SN - 9783037852316
VL - 325
T3 - Advanced Materials Research
SP - 345
EP - 350
BT - Advances in Abrasive Technology XIV
ER -