이동형 양불 판정 시스템 개발
Development of Movable Quality Monitoring System
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this study, we have developed a movable defect detection system based on a vision module with machine-learning algorithm for distinguishing product quality. Machine-learning model determined the results in good or no good through images acquired from the vision module consisting of a camera, processor unit, and lighting. To ensure versatility for use in a variety of settings, we have integrated a robot arm and cart for the movable defect detection system, and the robot arm that adjusts the focus length is made to be able to rotate in all directions. The type of defect was divided into eccentricity defect and printing defect. As a result, it was confirmed that classification accuracy showed 0.9901 in our developed device.
Abstract
In this study, we have developed a movable defect detection system based on a vision module with machine-learning algorithm for distinguishing product quality. Machine-learning model determined the results in good or no good through images acquired from the vision module consisting of a camera, processor unit, and lighting. To ensure versatility for use in a variety of settings, we have integrated a robot arm and cart for the movable defect detection system, and the robot arm that adjusts the focus length is made to be able to rotate in all directions. The type of defect was divided into eccentricity defect and printing defect. As a result, it was confirmed that classification accuracy showed 0.9901 in our developed device.
참고문헌 (15)
- 이익권, "중소기업 스마트팩토리 도입의 중요도 분석", 무역금융보험연구, 24(1), pp.143~153 (2023), 한국무역보험학회[DOI]
- S. R. Kim, "A study on the public support policies for AI-enabled smart factories", The Korean Society of Manufacturing Technology Engineers, pp.96~96 (2023)
- K. O. Cho, "Proposal of Human Error Reduction Method for Field workers at PSM Workplaces", Ergonomics Society of Korea, pp.144~144 (2023)
- 유민지, "머신러닝 알고리즘을 이용한 소형 플라스틱 렌즈 사출물의 변형 예측", 한국기계기술학회지, 24(5), pp.899~904 (2022), 한국기계기술학회[DOI]
- 정보성, "영상처리와 센서를 이용한 드론의 실내 장애물 인식 알고리즘 연구", 한국기계기술학회지, 24(6), pp.1138~1143 (2022), 한국기계기술학회[DOI]
- 권기남, "시력교정술 후 써클콘택트렌즈 착용시 double-pass 원리를 이용한 시력의 질 평가", 한국안광학회지, 20(3), pp.277~284 (2015), 한국안광학회
- G. N. Kim, "Detection of Contact Lens Defects using a Modified GoogLeNet", Journal of Korean Institute of Information Scientists and Engineers, pp.894~896 (2022)
- T. Y. Kim, "A Deep Learning Technique for Optical Inspection of Color Contact Lenses", Applied Sciences, 13(10), pp.5966~ (2023)
- 한승조, "정년연장이 청년고용 및 사업장 운용에 미치는 영향 분석 : 사회인식론 측면의 시민인식을 중심으로", 한국지식정보기술학회 논문지, 17(2), pp.237~246 (2022), 한국지식정보기술학회[DOI]
- 권선희, "생산가능인구 감소가 주택가격에 미치는 영향", 유라시아연구, 18(2), pp.121~141 (2021), 아시아.유럽미래학회[DOI]
- 김기남, "다양한 CNN모델을 사용한 컬러 콘택트렌즈 불량 검출", 한국콘텐츠학회 논문지, 22(12), pp.160~170 (2022), 한국콘텐츠학회[DOI]
- Kaiming He, "Deep Residual Learning for Image Recognition", Conference on Computer Vision and Pattern Recognition (CVPR), pp.770~778 (2016)
- Jia Deng, "Imagenet : A large-scale hierarchical image database", 2009 IEEE conference on computer vision and pattern recognition, pp.248~255 (2009)
- Karen Simonyan, "Very Deep Convolutional Networks for Large-Scale Image Recognition", Conference on Computer Vision and Pattern Recognition(CVPR) (2014)
- Alex Krizhevsky, "ImageNet Classification with Deep Convolutional Neural Networks", NeurIPS (2012)