• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3376-3382
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• 2014

The system developed inspects automatically defects existing on an inner surface of anodically treated aluminum cylinder-tubes. This system consists of automatic inspection software, and electronic endoscope and a conveyor moving device. By applying the optimal brightness conditions to searching for various defects on the inner surface of cylinder tube, the recognition rate of major defects, such as scratch, oxide and weld line reached 99%. If the present visual inspection process is replaced with the automatic defects inspection system, the physical fatigue of the operator could be reduced and the productivity could be increased. The automatic inspection system developed could also improve the quality of the products.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.60-70
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• 1999

An efficient automatic inspection system for Floating Seal is developed, The proposed system consists of a high resolution line scan camera, microcomputer, and PLC (Programmable Logic Controller). In order to perform rapid inspection, The ROI (Region of Interest) is extracted from the original image. There are types of defects; shape defects and surface defects. Each features of defects are captured by edge detect, segmentation, morphological operation, and threshold analysis. PLC controller is used to synchronize the whole system and store the inspection results temporarily to reduce the overhead of microcomputer. As a result, the system is being utilized successfully in a teal inspection line.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.5
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• pp.666-672
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• 2011

In this paper, we describes the development of automatic inspection system for detecting the defects on photovoltaic wafer by using machine vision. Until now, The defect inspection process was manually performed by operators. So these processes caused the produce of poorly-made articles and inaccuracy results. To improve the inspection accuracy, the inspection system is not only configured, but the image processing algorithm is also developed. The inspection system includes dimensional verification and pattern matching which compares a 2-D image of an object to a pattern image the method proves to be computationally efficient and accurate for real time application and we confirmed the applicability of the proposed method though the experience in a complex environment.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.444-452
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• 2008

This paper presents an overall image processing approach of defect inspection of patterned TFT-LCD panels for the real manufacturing process. A prototype of AOI(Automatic Optical Inspection) system which is composed of air floating stage and multi line scan cameras is developed. Adjacent pattern comparison algorithm is enhanced and used for pattern elimination to extract defects in the patterned image of TFT-LCD panels. New region merging algorithm which is based on border expansion is proposed to identify defects from the pattern eliminated defect image. Experimental results show that a developed AOI system has acceptable performance and the proposed algorithm reduces environmental effects and processing time effectively for applying to the real manufacturing process.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.35.3-35
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• 2001

Developed is an optical auto-inspection system to detect some microscopic defects on the Inside surface of the hydraulic automobile brakes at the production line. A small cylindrical detection module with a solid laser source at its center has two rings of optical fibers to separately collect light reflected and scattered from the defects on the surface. The cylindrical brake part rotates with respect to the detection module that will move parallel to the rotational axis of the cylinder. Thus, the optical module can scan the whole inside surface area. The automatic detection of the defects is to compare the intensity distributions ...

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.305-310
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• 2003

In this paper we deal with the system design and development of the high-speed automatic precision inspection for the defects of bolts. In order to inspect bolts continuously, we used the conveyor system. Also, this conveyor includes the servo motor and encoder to achieve accurate movement. According to encoder signal, line-scan camera captures the line-by-line image of bolts and after one frame is accumulated, various parameters are calculated and inspected by image processing algorithms. Experimental results using the developed facilities are presented to demonstrate the efficiency of the proposed equipment.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.22-29
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• 2022

In this study, an automatic marking system for fabric inspection machines was developed. The main objectives of the study were to promote intelligence and automation for the inspection process, as well as to increase textile industrial productivity. Generally, when a worker manually inspects and marks a fabric, human error and reduced efficiency are unavoidable. To overcome these problems, we developed an automatic marking system that uses robots. This system incorporates a vision camera to automatically recognize defects, and an optical fiber sensor to detect the side of the fabric. To verify the performance, the control system sends a command directly to the robot to mark the fabric. Finally, the actual production confirmed that the proposed system could perform the desired motion.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.959-962
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• 1988

This paper describes an automatic visual inspection system for fabric defects based on pattern recognition techniques. The inspection for fabric defects can be separated into three sequences of operations which are the detection of fabric defects[1], the classification of figures of fabric defects, and the classification of fabric defects. Comparing projections of defect-detected images with the predefined complex, the classification accuracy of figures of fabric defects was found to be 95.3 percent. Employing the Bayes classifier using cluster shade in SGLDM and variance in decorrelation method as features, the classification accuracy of regional figure defects was found to be 82.4 percent. Finally, some experimental results for line and dispersed figures of fabric defects are included.

• Smart Media Journal
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• v.11 no.9
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• pp.47-55
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• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.438-443
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• 2008

In this paper, segmenting and classifying low contrast defects on flat panel display is one of the key problems for automatic inspection system in practice. Problems become more complicated when the quality of acquired image is degraded by the illumination irregularity. Many algorithms are developed and implemented successfully for the defects segmentation. However, vision algorithms are inherently prone to be dependent on parameters to be set manually. In this paper, one morphological segmentation algorithm is chosen and a technique using frequency domain analysis of input images is developed for automatically selection the morphological parameter. An extensive statistical performance analysis is performed to compare the developed algorithms.