Abstract
Machining is often performed by a machining center using various cutting tools and end-milling conditions for different shapes and materials. Recent improvements in CAM system make it easier for even unskilled engineers to generate NC programs. In the NC program, the end-milling conditions are decided by engineers. However, engineers need to decide the order of the process, cutting tool selection, and the end-milling conditions on the basis of their expertise and background knowledge because the CAM system cannot automatically decide them. Data-mining methods were attracted attention to support decisions about end-milling conditions. Our aim was to extract new knowledge by applying data-mining techniques to a tool catalog. We used both hierarchical and non-hierarchical clustering methods and also principal component regression. We focused on the shape element of catalog data and we visually clustered ball end-mills from the viewpoint of tool shape, which here meant the ratio of dimensions, by using the k - means method. Expressions for calculating end-milling conditions were derived from response surface method. We conducted end-milling experiments to validate the availability of calculated values.
| Original language | English |
|---|---|
| Pages (from-to) | 964-969 |
| Number of pages | 6 |
| Journal | Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering |
| Volume | 79 |
| Issue number | 10 |
| Publication status | Published - Oct 2013 |
| Externally published | Yes |
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Keywords
- Ball end-mill
- Data-mining
- End-milling condition
- Hierarchical and non-hierarchical clustering
- Response surface method
ASJC Scopus subject areas
- Mechanical Engineering
Cite this
Proposal of ball end-milling condition decision methodology using data-mining from tool catalog data. / Kodama, Hiroyuki; Hirogaki, Toshiki; Aoyama, Eiichi; Ogawa, Keiji.
In: Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering, Vol. 79, No. 10, 10.2013, p. 964-969.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Proposal of ball end-milling condition decision methodology using data-mining from tool catalog data
AU - Kodama, Hiroyuki
AU - Hirogaki, Toshiki
AU - Aoyama, Eiichi
AU - Ogawa, Keiji
PY - 2013/10
Y1 - 2013/10
N2 - Machining is often performed by a machining center using various cutting tools and end-milling conditions for different shapes and materials. Recent improvements in CAM system make it easier for even unskilled engineers to generate NC programs. In the NC program, the end-milling conditions are decided by engineers. However, engineers need to decide the order of the process, cutting tool selection, and the end-milling conditions on the basis of their expertise and background knowledge because the CAM system cannot automatically decide them. Data-mining methods were attracted attention to support decisions about end-milling conditions. Our aim was to extract new knowledge by applying data-mining techniques to a tool catalog. We used both hierarchical and non-hierarchical clustering methods and also principal component regression. We focused on the shape element of catalog data and we visually clustered ball end-mills from the viewpoint of tool shape, which here meant the ratio of dimensions, by using the k - means method. Expressions for calculating end-milling conditions were derived from response surface method. We conducted end-milling experiments to validate the availability of calculated values.
AB - Machining is often performed by a machining center using various cutting tools and end-milling conditions for different shapes and materials. Recent improvements in CAM system make it easier for even unskilled engineers to generate NC programs. In the NC program, the end-milling conditions are decided by engineers. However, engineers need to decide the order of the process, cutting tool selection, and the end-milling conditions on the basis of their expertise and background knowledge because the CAM system cannot automatically decide them. Data-mining methods were attracted attention to support decisions about end-milling conditions. Our aim was to extract new knowledge by applying data-mining techniques to a tool catalog. We used both hierarchical and non-hierarchical clustering methods and also principal component regression. We focused on the shape element of catalog data and we visually clustered ball end-mills from the viewpoint of tool shape, which here meant the ratio of dimensions, by using the k - means method. Expressions for calculating end-milling conditions were derived from response surface method. We conducted end-milling experiments to validate the availability of calculated values.
KW - Ball end-mill
KW - Data-mining
KW - End-milling condition
KW - Hierarchical and non-hierarchical clustering
KW - Response surface method
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UR - http://www.scopus.com/inward/citedby.url?scp=84887442482&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84887442482
VL - 79
SP - 964
EP - 969
JO - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
JF - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
SN - 0912-0289
IS - 10
ER -