Deep Learning-Based 3D Printer Fault Detection

被引：13

作者：

Verana, Mark ^{[1
]}

Nwakanma, Cosmas Ifeanyi ^{[1
]}

Lee, Jae Min ^{[1
]}

Kim, Dong Seong ^{[1
]}

机构：

[1] Kumoh Natl Inst Technol, Dept IT Convergence Engn, Networked Syst Lab, Gumi, South Korea

来源：

12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2021) | 2021年

基金：

新加坡国家研究基金会;

关键词：

Convolutional neural network (CNN); 3D printer; fault diagnosis; deep learning;

D O I：

10.1109/ICUFN49451.2021.9528692

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The development of intelligent manufacturing and 3D printers is rapidly engaging in the industry. However, 3D printers are challenged by occasional anomalies due to leading to failure in 3D performance. In this work, a fault diagnosis based on a convolutional neural network (CNN) for 3D printers is proposed. We have leveraged an online repository of a set of data streams collected from working 3D printers. The CNN was used to process, detect and classify anomalies in 3D printing with appreciable accuracy. The proposed CNN outperformed the support vector machine (SVM), and artificial neural network (ANN) by 5.1% and 25.7%, respectively.

引用

页码：99 / 102

页数：4

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