Twinning IZTECH in Robotics Manufacturing Systems

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Project Title

Twinning IZTECH in Robotics Manufacturing Systems

Project Investigator

Mehmet İsmet Can Dede

Project Coordinator

Mehmet İsmet Can Dede

Project Coinvestigators

Project No

101160215

Project Start Date

2024-10-01

Project End Date

2027-09-30

Project Duration

4 Year

Project Funders

European Union

Project Funding Program

Horizon 2020

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Project Internal ID

101160215

Open Access Mandate

Handle URL

https://hdl.handle.net/11147/18911

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Conference Object
A Comparative Study of Attention-Augmented YOLO Architectures for Defect Detection in Fused Deposition Modelling
(Institute of Electrical and Electronics Engineers Inc., 2025) Cezayirli, H.; Tetik, H.; Dede, M.I.C.; Phone, W.L.; Alkan, B.
Additive manufacturing (AM), particularly fused deposition modelling (FDM), facilitates the fabrication of complex geometries with increasing flexibility and efficiency. Ensuring consistent print quality in FDM processes necessitates the development of accurate defect detection mechanisms. Attention-augmented YOLO (You Only Look Once) models have emerged as a promising solution for addressing this challenge. In this study, we systematically benchmark and evaluate the performance of YOLO architectures enhanced with attention mechanisms within the context of FDM 3D printing applications. The models were trained and evaluated using representative defect datasets. The attention-augmented models demonstrate improved detection performance. © 2025 IEEE.

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