Abstract
Data-mining methods were applied to support decisions about reasonable micro end-mill cutting conditions (cutting speed, feed rate, axial depth of cut and radius depth of cut). The aim of this research was to excavate new knowledge with the mining effect by applying the data mining process of hierarchical and non-hierarchical clustering methods to micro end-mill tool catalogs. Micro end-mill shape element of catalog data were focused on visually grouped end-mills, which meant the ratio of tool shape dimensions. With these process, principal component analysis was used to quantify the correlation degree between cutting conditions and tool shape parameters. End-milling condition decision equations were derived from response surface method using significant predictor variables consisting of tool shape parameters and workpiece mechanical properties. The catalog-mining system appeared to be effective for mining knowledge hidden in a large amount of catalog data related to tool shape and end-milling conditions. Therefore, it appears to be straightforward for unskilled engineers to visually determine micro end-milling conditions from the tool shape. Moreover, short-delivery manufacturing with less waste may be possible.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016 |
| Publisher | euspen |
| ISBN (Electronic) | 9780956679086 |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016 - Nottingham, United Kingdom Duration: May 30 2016 → Jun 3 2016 |
Other
| Other | 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016 |
|---|---|
| Country | United Kingdom |
| City | Nottingham |
| Period | 5/30/16 → 6/3/16 |
Fingerprint
Keywords
- Data-mining
- Hierarchical and non- hierarchical clustering
- Micro end-mill
- Tool catalog data
ASJC Scopus subject areas
- Materials Science(all)
- Environmental Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Instrumentation
Cite this
Decision methodology of micro end-milling condition using tool catalog data-mining system. / Kodama, Hiroyuki; Okuda, Koichi; Tsujimoto, Takuya.
Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016. euspen, 2016.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Decision methodology of micro end-milling condition using tool catalog data-mining system
AU - Kodama, Hiroyuki
AU - Okuda, Koichi
AU - Tsujimoto, Takuya
PY - 2016
Y1 - 2016
N2 - Data-mining methods were applied to support decisions about reasonable micro end-mill cutting conditions (cutting speed, feed rate, axial depth of cut and radius depth of cut). The aim of this research was to excavate new knowledge with the mining effect by applying the data mining process of hierarchical and non-hierarchical clustering methods to micro end-mill tool catalogs. Micro end-mill shape element of catalog data were focused on visually grouped end-mills, which meant the ratio of tool shape dimensions. With these process, principal component analysis was used to quantify the correlation degree between cutting conditions and tool shape parameters. End-milling condition decision equations were derived from response surface method using significant predictor variables consisting of tool shape parameters and workpiece mechanical properties. The catalog-mining system appeared to be effective for mining knowledge hidden in a large amount of catalog data related to tool shape and end-milling conditions. Therefore, it appears to be straightforward for unskilled engineers to visually determine micro end-milling conditions from the tool shape. Moreover, short-delivery manufacturing with less waste may be possible.
AB - Data-mining methods were applied to support decisions about reasonable micro end-mill cutting conditions (cutting speed, feed rate, axial depth of cut and radius depth of cut). The aim of this research was to excavate new knowledge with the mining effect by applying the data mining process of hierarchical and non-hierarchical clustering methods to micro end-mill tool catalogs. Micro end-mill shape element of catalog data were focused on visually grouped end-mills, which meant the ratio of tool shape dimensions. With these process, principal component analysis was used to quantify the correlation degree between cutting conditions and tool shape parameters. End-milling condition decision equations were derived from response surface method using significant predictor variables consisting of tool shape parameters and workpiece mechanical properties. The catalog-mining system appeared to be effective for mining knowledge hidden in a large amount of catalog data related to tool shape and end-milling conditions. Therefore, it appears to be straightforward for unskilled engineers to visually determine micro end-milling conditions from the tool shape. Moreover, short-delivery manufacturing with less waste may be possible.
KW - Data-mining
KW - Hierarchical and non- hierarchical clustering
KW - Micro end-mill
KW - Tool catalog data
UR - http://www.scopus.com/inward/record.url?scp=84984602113&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84984602113&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84984602113
BT - Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016
PB - euspen
ER -