3DVis: A Layer-wise Fused Deposition Modeling 3D Printer Fault Detection Dataset | IEEE DataPort

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 Parama...
Last updated:
Wed, 01/11/2023 - 02:56
DOI:
10.21227/wb76-fb38
Data Format:
Image files (.png)
Research Article Link:
Efficient 3D Printer Fault Classification Using a Multi-Block 2D-Convolutional Neural Network
Links:
3DFed: A Secure Federated Learning-based System for Fault Detection in 3D Printer Industry
Fine-Tuned CNN with Data Augmentation for 3D Printer Fault Detection
License:
Creative Commons Attribution
1877 Views
Categories:
Artificial Intelligence
Machine Learning
Image Processing
Computer Vision
Keywords:
3D Printer, additive manufacturing, Deep Learning, Fault detection, Fused Deposition Modeling
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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

Comments

Thanks.

Submitted by Utku ATAMAN on Thu, 05/30/2024 - 04:11

For research with my master's student, we want to use this dataset. Thank you

Submitted by BURCU YILMAZEL on Thu, 05/30/2024 - 04:12

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