반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

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

딥러닝 알고리즘을 이용한 3D프린팅 골절합용 판의 표면 결함 탐지 모델에 관한 연구

A Study on Surface Defect Detection Model of 3D Printing Bone Plate Using Deep Learning Algorithm

  • 이송연 (한국기술교육대학교 대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Lee, Song Yeon (Department of 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 : 2022.06.04
  • 심사 : 2022.06.23
  • 발행 : 2022.06.30
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초록

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

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참고문헌

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