반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

CNN 알고리즘을 이용한 인공지지체의 3D프린터 출력 시 실시간 출력 불량 탐지 시스템에 관한 연구

A Study on Real-Time Defect Detection System Using CNN Algorithm During Scaffold 3D Printing

  • 이송연 (한국기술교육대학교 대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Lee, Song Yeon (Mechatronics Engineering, Graduate School of Korea University of Technology and Education) ;
  • Huh, Yong Jeong (School of Mechatronics Engineering, Korea University of Technology and Education)
  • 투고 : 2021.09.03
  • 심사 : 2021.09.16
  • 발행 : 2021.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

키워드

참고문헌

  1. hang-Hee Lee, Min-Woo Sa, Seung-Hyuk Choi and Jong-Young Kim, "Development of a Novel Laser Sintering Deposition System for Fabrication of 3D Bio-Ceramic Scaffold", J. of The Korean Society of Mechanical Engineers, Vol.43, pp. 513-520, 2019. https://doi.org/10.3795/KSME-A.2019.43.8.513
  2. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Shape Warpage Defect Detection Model of Scaffold Using Deep Learning Based on CNN", J. of The Korean Society of Semiconductor & Display Technology, Vol.20, pp. 99-103, 2021.
  3. Song-Yeon Lee and Yong-Jeong Huh, "A Comparative Study on Deep Learning Models for Scaffold Defect Detection", J. of The Korean Society of Semiconductor & Display Technology, Vol.20, pp. 109-114, 2021.
  4. Jung-Hee Han and Sung-Soo Hong"Semiconductor Process Inspection Using Mask R-CNN", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 12-14, 2020.
  5. Soo-Hyeon Lee, Dong-Hyun Kim and Hae-Yeoun Lee, "Camera Model Identification Using Modified DenseNet and HPF", J. of Korean Institute of Information Technology, Vol.17, pp. 11-19, 2019
  6. Shin-Young Ahn, Eun-Ji Lim and Wan Choi, "Trends on Distributed Frameworks for Deep Learning" Electronics and Telecommunications Trends, Vol.31, pp.131-141, 2016.
  7. Soo-Hyeon Lee, Dong-Hyun Kim and Hae-Yeoun Lee, "Camera Model Identification Using Modified DenseNet and HPF", J. of Korean Institute of Information Technology, Vol.17, pp. 11-19, 2019
  8. Yong-Hwan Lee and Young-Sub Kim, "Comparison of CNN and YOLO for Object Detection" J. of The Korea Society of Semiconductor & Display Technology, Vol.19, pp.85-92, 2020.