• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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• pp.87-92
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• 2006

This paper supplements shortcoming of radioactivity check by detecting defect of SWP weld zone using ultrasonic wave. Manufacture 2 stage robot detection systems that can follow weld bead of SWP by method to detect weld defects of SWP that shape of weld bead is complex for this as quantitative. Also, through signal processing ultrasonic wave defect signal system of GUI environment that can grasp easily existence availability of defect because do videotex compose. Ultrasonic wave signal of weld defects develops artificial intelligence style sightseeing system to enhance pattern recognition of weld defects and the classification rate using neural net. Classification of weld defects that do fan Planar defect and that do volume defect of by classify.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.57-61
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• 2013

This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.17 no.4
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• pp.84-88
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• 2013

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.448-455
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• 2003

A system design technique of automatic thickness measurement and defect inspection system, which measures the thickness of the ALC(Autoclaved Lightweight Concrete) block and inspects the defect on a realtime basis is proposed. The image processing system was established with a CCD camera, an image grabber, and a personal computer without using assembled measurement equipment. The image obtained by this system was analyzed by a devised algorithm, specially designed for the enhanced measurement accuracy. For the realization of the proposed algorithm, the preprocessing method that can be applied to overcome uneven lighting environment, an enhanced edge decision method using 8 edge-pairs with irregular and rough surface, the unit length decision method in uneven condition with rocking objects, and the curvature calibration method of camera using a constructed grid are developed. The experimental results, show that the required measurement accuracy specification is sufficiently satisfied using our proposed method.

• Journal of Ocean Engineering and Technology
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• v.20 no.6 s.73
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• pp.1-6
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• 2006

Corrosion of reinforcing steel bars is a major concern for ocean engineers when reinforced concrete structures are exposed to marine environments. Evaluating the degree of corrosion and corrosion-induced defects is extremely necessary to pursue a proper retrofit or rehabilitation plan for reinforced concrete structures. A promising inspection should be carried out for the evaluation, otherwise the retrofit or rehabilitation process would be useless. Nowadays, ultrasonic guided wave-based inspection techniques become quite promising for the inspection, mainly because of their long-range propagation capability and their sensitivity to different types of defects or conditions. Evaluating haw the guided waves response to the different types of defects or conditions is quite challenging and important. This study shows how surface conditions of reinforcing bars and a corrosion-induced defect, separation, affect guided wave propagation in reinforced concrete. Experiments and associated signal analysis show the sensitivity of guided waves to the surface conditions, as well as the amounts of separation at the interface between. concrete and steel bar.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2738-2740
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• 2001

This paper describes a study on implementation of image processing systems for the defect inspection of polyethylene. In order to detect the edge, the Robert filter is used. And we use to the labeling algorithm for feature extraction. Labeling the conected regions of a image is a fundamental computation in image analysis and machine vision, with a large number of application. This algorithm is designed for the defect inspection of polyethylene.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.112-116
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• 2007

If any of the set parameters such as the environment temperature, mold temperature are not maintained at a consistent level, the fail rate of injection molding products is increased. The price of the injection molding machine is very high, so in order to maximize the utilization of the machine that is required the production of a number of different products with minimum fail rate using a single machine. To prevent the defect products by an inspection process with perfect quality is very important to minimizing production of defect products in the molding process. Vision inspection systems are widely utilized in various manufacturing industries for quality assurance purposes. The vision inspection system consists of CCD camera and lighting system to capture the image of the subject of inspection, an image comparison algorithm using to determine the pass/fail of the products, and mechanical devices for the operation of the whole system. This research focuses on the development of the vision inspection system to process the inspection of an automobile parts. We developed a mechanical devices for the inspection of the injection molding products and an image comparison algorithm to determine the pass/fail result of the inspection based on the molding image and the accepted product image.

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.72-77
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• 2003

Visual inspection works are very important part of facility maintenance and management life cycle as a step of creating base data for decision making and maintenance and repair. But currently, visual inspection works have been performed unefficiently and unreliably as works on hand, duplication of works, decisions of defect state, unutilization of inspection history, lack of professional and so on. In this research, we developed visual inspection system that processes inspection work efficiently and provides reliability of inspection output, as a target of bridges, tunnels, underground roadway on national road. We defined problems through visual inspection work analysis, examined the solutions and reflected to functional design of system. We will plan to utilize this system in HMCS(Highway Maintenance and Construction Service).

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.139-142
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• 2000

$\mu$BGA(Ball Grid Array) is growing in response to a great demand for smaller and lighter packages for the use in laptop, mobile phones and other evolving products. However it is not easy to find its defect by human visual due to in very small dimension. From this point of view, we are interested its development of a vision based automated inspection algorithm. For this, first a 2D view of $\mu$BGA is described under a special blue illumination. Second, a notation-invariant 2D inspection algorithm is developed. Finally a 3D inspection algorithm is proposed for the case of stereo vision system. As a simulation result, it is shown that 3D defect not easy to find by 2D algorithm can be detected by the proposed inspection algorithm.

• Textile Coloration and Finishing
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• v.21 no.1
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• pp.53-58
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• 2009

A real-time inspection system has been developed by combining CCD based image processing algorithm and a standard lighting equipment. The system was tested for defective fabrics showing nozzle contact scratch marks, which were one of the frequently occurring defects. Multi-resolution analysis(MRA) algorithm were used and evaluated according to both their processing time and detection rate. Standard value for defective inspection was the mean of the non-defect image feature. Similarity was decided via comparing standard value with sample image feature value. Totally, we achieved defective inspection accuracy above 95%.