Efficient 3D Printer Fault Classification Using a Multi-Block 2D-Convolutional Neural Network 


Vol. 47,  No. 2, pp. 236-245, Feb.  2022
10.7840/kics.2022.47.2.236


PDF
  Abstract

This paper proposes a novel fault classification method with an efficient deep learning (DL) model with fast inference time and lower computational complexity during the 3D printer printing process. Specifically, a multi-block 2D-convolutional neural network (CNN) is used to classify the 3D printer fault. In the proposed method, blocks of CNNs are used to extract the features from an image dataset that is gathered with a FDM 3D printer type. The performance evaluation of the proposed model is compared with existing image classification algorithms, such as MobileNet, AlexNet, VGG-11, and VGG-16. The results show that the proposed multi-block CNN classification model yields high accuracy with 67.01% faster inference time, 87.56% lower memory usage, and lower trainable parameters up to 93.36%. Furthermore, the proposed 3D model can provide an accurate classification in real-time monitoring conditions.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

M. A. P. Putra, A. L. A. Chijioke, M. Verana, D. Kim, J. Lee, "Efficient 3D Printer Fault Classification Using a Multi-Block 2D-Convolutional Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 2, pp. 236-245, 2022. DOI: 10.7840/kics.2022.47.2.236.

[ACM Style]

Made Adi Paramartha Putra, Ahakonye Love Allen Chijioke, Mark Verana, Dong-Seong Kim, and Jae-Min Lee. 2022. Efficient 3D Printer Fault Classification Using a Multi-Block 2D-Convolutional Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 47, 2, (2022), 236-245. DOI: 10.7840/kics.2022.47.2.236.

[KICS Style]

Made Adi Paramartha Putra, Ahakonye Love Allen Chijioke, Mark Verana, Dong-Seong Kim, Jae-Min Lee, "Efficient 3D Printer Fault Classification Using a Multi-Block 2D-Convolutional Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 2, pp. 236-245, 2. 2022. (https://doi.org/10.7840/kics.2022.47.2.236)

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880