Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach-Reference-Cited by-同舟云学术

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Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach

Published:2023-04-17 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Systems Conference (SysCon)
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Author:

Mandapaka Subbalakshmi¹,Diaz Catalina¹,Irisson Hasbanny¹,Akundi Aditya²,Lopez Viviana³,Timmer Douglas³

Affiliation:

1. University of Texas Rio Grande Valley,Dept. of Computer Science,Texas,United States

2. University of Texas Rio Grande Valley,Informatics and Engineering Systems Dept.,Texas,United States

3. University of Texas Rio Grande Valley,Manufacturing and Industrial Eng.,Texas,United States

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10131045/10131046/10131100.pdf?arnumber=10131100

Reference8 articles.

1. A State-of-the-Art Survey on Deep Learning Theory and Architectures;alom;Electronics,2019

2. Deep residual learning for image recognition;he;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016

3. Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

4. A Quality Control Application on a Smart Factory Prototype Using Deep Learning Methods

5. Going deeper with convolutions

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence enabled self-powered wireless sensing for smart industry;Chemical Engineering Journal;2024-07

2. Flexible machine/deep learning microservice architecture for industrial vision-based quality control on a low-cost device;Journal of Electronic Imaging;2024-03-09

3. Intelligent Machine Vision Implementation for Production Quality Control;2023 34th Conference of Open Innovations Association (FRUCT);2023-11-15

4. A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing;Electronics;2023-11-08

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