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3DVis: A Layer-wise Fused Deposition Modeling 3D Printer Fault Detection Dataset
- Citation Author(s):
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Made Adi ParamarthaPutra
Department of IT Convergence Engineering, Kumoh National Institute of Technology, South KoreaDong-SeongKimDepartment of IT Convergence Engineering, Kumoh National Institute of Technology, South KoreaJae-MinLeeDepartment of IT Convergence Engineering, Kumoh National Institute of Technology, South Korea - Submitted by:
- Made Adi Parama...
- Last updated:
- Wed, 01/11/2023 - 02:56
- 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
