Design & Manufacturing

Korean Society of Die & Mold Engineering (한국금형공학회)
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Development of a transfer learning based detection system for burr image of injection molded products

전이학습 기반 사출 성형품 burr 이미지 검출 시스템 개발

  • 양동철 (심테크) ;
  • 김종선 (한국생산기술연구원 형상제조연구부문)
  • Received : 2021.09.09
  • Accepted : 2021.09.30
  • Published : 2021.09.30
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Abstract

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

Keywords

Acknowledgement

본 연구는 산업통산자원부의 소재부품산업기술 개발기반구축사업(Project No. KM210058, 20011822)의 지원으로 진행되었습니다.

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