• Smart Structures and Systems
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• 제25권6호
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• pp.733-749
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• 2020

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

• 한국정밀공학회지
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• 제31권3호
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• pp.261-268
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• 2014

Precision screws have a wide range of industrial applications such as electrical and automotive products. To produce screw threads with high precision, not only high precision manufacturing technology but also reliable measurement technology is required. Machine vision systems have been used in the automatic inspection of screw threads based on backlight illumination, which cannot detect defects on the thread surface. Recently, an omnidirectional inspection system for screw threads was developed to obtain $360^{\circ}$ images of screws, based on front light illumination. In this study, the illumination design for the omnidirectional inspection system was modified by adding a light shield to improve the image uniformity. Optical simulation for various shield designs was performed to analyze image uniformity of the obtained images. The simulation results were analyzed statistically using response surface method, from which optical performance of the omnidirectional inspection system could be optimized in terms of image quality and uniformity.

• 한국정밀공학회지
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• 제28권9호
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• pp.1094-1102
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• 2011

Recent trends for the miniaturization and weight reduction of portable electronic parts is the use of subminiature components. Assembly of the miniaturized components requires subminiature screws of which pitch sizes are in a micrometer scale. To produce such a subminiature screw with high precision threads, not only a precision forming technology but also high-precision measurement technique is required. In the present work, a vision inspection system is developed to measure the thread profile of a subminiature screw. Optical simulation based on a ray tracing method is used to design and analyze the optical system of the vision inspection apparatus. Through this simulation, optical performance of the developed vision inspection system is optimized. The image processing algorithm for the precision screw inspection is also discussed.

• 제어로봇시스템학회논문지
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• 제10권1호
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• pp.54-59
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• 2004

We suggest a 3D vision inspection algorithm which is based on the external shape feature. Because many electronic parts have the regular shape, if we have the database of pattern and can recognize the object using the database of the object s pattern, we can inspect many types of electronic parts. Our proposed algorithm uses the geometrical pattern matching method and 3D database on the electronic parts. We applied our suggested algorithm fer inspecting several objects including typical IC and capacitor. Through the experiments, we could find that our suggested algorithm is more effective and more robust to the inspection environment(rotation angle, light source, etc.) than conventional 2D inspection methods. We also compared our suggested algorithm with the feature space trajectory method.

• 전기학회논문지
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• 제67권7호
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• pp.865-870
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• 2018

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.

• 한국산학기술학회논문지
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• 제14권6호
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• pp.2624-2630
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• 2013

LED 칩 패키징에서 다이 본딩은 분할된 칩을 리드 프레임에 고정시켜 칩이 이후 공정을 견딜 수 있도록 충분한 강도를 제공하는 중요한 공정이다. 다이본딩 공정 중에서, 측정 단계는 정확한 에폭시 토출 위치 지정과 충분한 강도를 가지고 접합할 수 있도록 다이가 정확한 위치에 놓여있는지 상태를 결정하는데 있어 매우 중요하다. 본 연구에서는 LED 다이 본딩을 위한 머신 비전 기반의 측정 시스템을 개발하였다. 제안된 시스템에서 에폭시 토출과 어태칭 상태 검출을 위해 각각 2개의 카메라를 사용하였다. 제안된 측정 시스템에 새로운 비전 알고리즘을 적용하였고, 실험을 통해 본 알고리즘의 효율을 검증하였다. 비전 알고리즘을 이용하여 측정된 위치 오차는 $X:-29{\mu}m$, $Y:-32{\mu}m$, 회전오차는 3도 이내 인 것을 확인할 수 있었다. 결론적으로 제안된 머신 비전 기반의 측정 시스템을 통해 개발된 다이 본딩 시스템의 향상된 성능을 확인하였다.

• 센서학회지
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• 제32권5호
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• pp.320-327
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• 2023

In this study, an FPGA-based sealer inspection system was developed to inspect the sealer applied to install vehicle glass on a car body. The sealer is a liquid or paste-like material that promotes adhesion such as sealing and waterproofing for mounting and assembling vehicle parts to a car body. The system installed in the existing vehicle design parts line does not detect the sealer in the glass rotation section and takes a long time to process. This study developed a line laser camera sensor and an FPGA vision signal processing module to solve this problem. The line laser camera sensor was developed such that the resolution and speed of the camera for data acquisition could be modified according to the irradiation angle of the laser. Furthermore, it was developed considering the mountability of the entire system to prevent interference with the sealer ejection machine. In addition, a vision signal processing module was developed using the Zynq-7020 FPGA chip to improve the processing speed of the algorithm that converted the profile to the sealer shape image acquired from a 2D camera and calculated the width and height of the sealer using the converted profile. The performance of the developed sealer application inspection system was verified by establishing an experimental environment identical to that of an actual automobile production line. The experimental results confirmed the performance of the sealer application inspection at a level that satisfied the requirements of automotive field standards.

• 제어로봇시스템학회논문지
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• 제20권7호
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• pp.741-749
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• 2014

This paper proposes a machine vision module for PLCs (Programmable Logic Controllers). PLC is the industrial controller most widely used in factory automation system. However most of the machine vision systems are based on PC (Personal Computer). The machine vision system embedded in PLC is required to reduce the cost and improve the convenience of implementation. In this paper, we newly propose a machine vision module based on PLC. The image processing libraries are implemented and integrated with the PLC programming tool. In order to interface the libraries with ladder programming, the ladder instruction set was also designed for each vision library. By use of the developed system, PLC users can implement vision systems easily by ladder programming. The developed system was applied to sample inspection system to verify the performance. The experimental results show that the proposed system can reduce the cost of installing as well as increase the ease-of-implementation.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.974-978
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• 2013

AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. AVI systems usually report different measurements on the same defect with some variations on position or rotation mainly because different images are provided. This is caused by possible variations from the image acquisition process including optical factors, nonuniform illumination, random noises, and so on. For this reason, conventional area based defect measuring methods have problems of robustness and consistency. In this paper, we propose a new defect size measuring method to overcome this problem, utilizing volume information that is completely ignored in the area based defect measuring method. The results show that our proposed method dramatically improves the robustness and consistency of defect size measurement.