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http://dx.doi.org/10.6109/jkiice.2021.25.9.1144

Design and Implementation of a Real-Time Product Defect Detection System based on Artificial Intelligence in the Press Process  

Kim, Dong-Hyun (Dong-Nam Grand ICT R&D Center, Pusan National University)
Lee, Jae-Min (School of Computer Science and Engineering, Pusan National University)
Kim, Jong-Deok (School of Computer Science and Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.9, 2021 , pp. 1144-1151 More about this Journal
Abstract
The pressing process is a compression process in which a product is made by applying force to a heated or unheated material to transform it into the desired shape. Due to the characteristics of press equipment that produces products through continuous compression for a short time, product defects occur continuously, and systems for solving these problems are being developed using various technologies. This paper proposes a real-time defect detection system based on an artificial intelligence algorithm that detects defects. By attaching various sensors to the press device, the relationship between equipment status and defects is defined and collected based on a big data platform. By developing an artificial intelligence algorithm based on the collected data and implementing the developed algorithm using an embedded board, we will show the practicality of the system by applying it to the actual field.
Keywords
Artificial intelligence; Faulty detection; Manufacturing execution system; Smart factory; Press process;
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1 H. C. Lee, I. H. Park, T. H. Im, and D. T. Moon, "CNN-based Building Recognition Method Robust to Image Noises," Journal of The Korean Institute of Information and Communication Engineering, vol. 24, no. 3, pp. 341-348, Mar. 2020.
2 Raspberrypi project [Internet]. Available: https://www.raspberrypi.org/.
3 N. Adiga, D. Govind, and M. Prasanna, "Significance of epoch identification accuracy for prosody modification," in Proceeding of the 2014 International Conference on Signal Processing and Communications (SPCOM), Bangalore, India, 2014.
4 D. H. Kim, J. M. Lee, and J. D. Kim, "Design and Implementation of Real Time Device Monitoring and History Management System based on Multiple devices in Smart Factory," Journal of The Korean Institute of Information and Communication Engineering, vol. 25, no. 1, pp. 124-133, Jan. 2021.
5 S. G. Han, "Educational Contents for Concepts and Algorithms of Artificial Intelligence," The Korean Society of Computer and Information, vol. 26, no. 1, pp. 37-44, Jan. 2021.
6 J. W. Yun, "A Study for In-process Monitoring in Press die," Korea Academy Industrial Cooperation Society, vol. 18, no. 6, pp. 692-696, Jun. 2017.
7 Y. J. Jeon, H. S. Choi, Y. B. Ko, and D. E. Kim, "Perform Process Design to Form Sharp Wedges on Both Surfaces of Anti-loosening Washer," The Korean Society Of Automotive Engineers, vol. 28, no. 12, pp. 897-901, Dec. 2020.   DOI
8 D. H. Kim, S. B. Boo, H. C. Hong, W. G. Yeo, and N. Y. Lee, "Machine Vision-based Defect Detection Using Deep Learning Algorithm," Journal of The Korean Society for Nondestructive Testing, vol. 40, no. 1, pp. 47-52, Feb. 2020.   DOI
9 H. J. Shin, N. J. Kwak, and T. S. Song, "Detection The Behavior of Smartphone Users using Time-division Feature Fusion Convolutional Neural Network," Journal of The Korean Institute of Information and Communication Engineering, vol. 24, no. 9, pp. 1224-1230, Sep. 2020.
10 OPC FOUNDATION [Internet]. Available: https://opcfoundation.org/.
11 EMERSON AUTOMATION SOLUTIONS [Internet]. Available: https://www.emerson.com/en-gb/.