| dc.creator |
Ozsoy, Koray |
|
| dc.creator |
AKSOY, BEKİR |
|
| dc.creator |
Bayrakci, Hilmi Cenk |
|
| dc.date |
2022-01-01T00:00:00Z |
|
| dc.date.accessioned |
2022-05-10T11:27:56Z |
|
| dc.date.available |
2022-05-10T11:27:56Z |
|
| dc.identifier |
aa1661aa-de4d-4a16-ae94-1984ed1680c2 |
|
| dc.identifier |
10.1520/jte20210183 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/aa1661aa-de4d-4a16-ae94-1984ed1680c2/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/96856 |
|
| dc.description |
In this study, the cooler type produced with a fused deposition modeling (FDM) 3-D printer, one of the 3-D printing technologies, was investigated using image processing techniques and machine learning algorithms. This study aims to change the cooler design concept used in FDM 3-D printers and use image processing techniques and innovative machine learning algorithms to solve the temperature effect problems on the part. In this study, four different cooler types - no-cooler, A-type, B-type, and C-type-were used with an FDM 3-D printer, and each layer processing image of these parts was captured with a thermal camera. Temperature distribution diagrams of the parts were drawn according to layers using image processing techniques such as the Gaussian filtering method and the Sobel and Canny edge detection techniques. Using three different machine learning algorithms on the temperature data set obtained from the experimental study, cooler types were classified with an accuracy of over 90 %. The results showed that using machine learning algorithms, the most suitable cooler type can be selected with an accuracy of 95 % by the Extreme Gradient Boosting (XGBOOST) algorithm. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Optimization of Thermal Modeling Using Machine Learning Techniques in Fused Deposition Modeling 3-D Printing |
|
| dc.type |
info:eu-repo/semantics/article |
|