3DVis: A Layer-wise Fused Deposition Modeling 3D Printer Fault Detection Dataset
- Citation Author(s):
-
Made Adi Paramartha Putra
(Department of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea)
Love Allen Chijioke Ahakonye (Department of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea)Mark Verana (Department of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea)Syifa Maliah Rachmawati (Department of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea)Gabriel Avelino Sampedro (Department of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea)Dong-Seong Kim
(Department of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea)
Jae-Min Lee
(Department of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea)
- Submitted by:
- Made Adi Paramartha Putra
- Last updated:
- DOI:
- 10.21227/wb76-fb38
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Abstract
Recently, a limited number of datasets that exist are used to detect errors in the printing process of the 3D printer. Limited datasets lead most researchers to dive into sensor data fault classification.
The dataset is captured and labelled before being fed to the DL model. The image dataset is captured in a time-lapse video mode with a 15-second duration for each printing process. Next, the time-lapse is used to extract around 50 images per video. In total, 2297 images containing four classes are collected.
Moreover, data augmentation is conducted to produce additional data for each class. Finally, the total image of 4261 is presented in this dataset.
Instructions:
3D Printer image dataset
Thanks.
For research with my master's student, we want to use this dataset. Thank you