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Designing a quality inspection system using Deep SVDD 1)

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(11), pp.21-28
  • DOI : 10.9708/jksci.2023.28.11.021
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 18, 2023
  • Accepted : November 9, 2023
  • Published : November 30, 2023

Jungjun Kim 1 Sung-Chul Jee 1 Seungwoo Kim 1 Kwang-Woo Jeon ORD ID 1 Jeon-Sung Kang 1 Hyun-Joon Chung 1

1한국로봇융합연구원

Accredited

ABSTRACT

In manufacturing companies that focus on small-scale production of multiple product varieties, defective products are manually selected by workers rather than relying on automated inspection. Consequently, there is a higher risk of incorrect sorting due to variations in selection criteria based on the workers' experience and expertise, without consistent standards. Moreover, for non-standardized flexible objects with varying sizes and shapes, there can be even greater deviations in the selection criteria. To address these issues, this paper designs a quality inspection system using artificial intelligence-based unsupervised learning methods and conducts research by experimenting with accuracy using a dataset obtained from real manufacturing environments.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.