| dc.creator |
ÇOLAK, Oğuz |
|
| dc.creator |
KURBANOĞLU, Cahit |
|
| dc.creator |
Kayacan, Mehmet Cengiz |
|
| dc.date |
2006-12-31T22:00:00Z |
|
| dc.date.accessioned |
2020-10-06T11:11:41Z |
|
| dc.date.available |
2020-10-06T11:11:41Z |
|
| dc.identifier |
b92a8409-ba94-4f62-a632-dd39447fb401 |
|
| dc.identifier |
10.1016/j.matdes.2005.07.004 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/b92a8409-ba94-4f62-a632-dd39447fb401/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/70383 |
|
| dc.description |
CNC milling has become one of the most competent, productive and flexible manufacturing methods, for complicated or sculptured surfaces. In order to design, optimize, built up to sophisticated, multi-axis milling centers, their expected manufacturing output is at least beneficial. Therefore data, such as the surface roughness, cutting parameters and dynamic cutting behavior are very helpful.. especially when they are computationally produced, by artificial intelligent techniques. Predicting of surface roughness is very difficult using mathematical equations. In this study gene expression programming method is used for predicting surface roughness of milling surface with related to cutting parameters. Cutting speed, feed and depth of cut of end milling operations are collected for predicting surface roughness. End of the study a linear equation is predicted for surface roughness related to experimental study. (c) 2005 Elsevier Ltd. All rights reserved. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Milling surface roughness prediction using evolutionary programming methods |
|
| dc.type |
info:eu-repo/semantics/article |
|