• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.101-106
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• 2020

This research is related to a development of the ultrasonic detector for an internal defect detection of various assembly part's welding zone. In this research, measurement S/Ws including system's motion control, S/W ultrasonic transmitter/receiver control, defect judgment standard setting, etc. have been designed for ultrasonic detection, and welding defects sample network, etc. were also designed for comparison between products in good condition and defective products. Through this kind of system, automatic detection function can be performed for the depth and the defect location of the assembly parts welding zone, and the system is able to make a judgment of internal defect detection which is used to be performed by an expert in the past.

• Journal of IKEEE
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• v.25 no.4
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• pp.689-696
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• 2021

In this paper, in order to apply Prognostics and Health Management(PHM) to an electronic system or circuit, a circuit capable of detecting and predicting defect characteristics inside the system or circuit is implemented, and the results are described. In the previous study, we demonstrated that the frequency of the amplitude of S-parameter changed as the circuit defect progressed. These characteristics were measured by network analyser. but in this study, even if the same defect detection method is used, a circuit is proposed to check the progress of the defect, the remaining time, and the occurrence of the defect without large measurement devices. The circuit is designed to detect the change in impedance that generates changes of S-parameter, and it is verified through simulation using the measurement results of Bond-wires.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.88-93
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• 2021

The automotive industry is developing day by day. Among them, it is very important to prevent accidents while driving. However, despite the importance of developing automobile industry technology, accidents due to road defects increase every year, especially in the rainy season. To this end, we proposed a road defect detection system for road management by converging deep learning and raspberry pi, which show various possibilities. In this paper, we developed a system that visually displays through a map after analyzing the images captured by the Raspberry Pi and the route GPS. The deep learning model trained for this system achieved 96% accuracy. Through this system, it is expected to manage road defects efficiently at a low cost.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.171-171
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• 2000

We have developed m innovative surface inspection system for automated quality control for steel products in POSCO. We had ever installed the various kinds of surface inspection systems, such as a linear CCD and a laser typed surface inspection systems at cold rolled strips production lines. But, these systems cannot fulfill the sufficient detection and classification rate, and real time processing performance. In order to increase detection and classification rate, we have used the Dark, Bright and Transition Field illumination and area type CCD camera, and fur the real time image processing, parallel computing has been used. In this paper, we introduced the automatic surface inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms and its performance obtained at the production line.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.92-99
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• 2004

The formation degree of sustain (ITO pattern) decides quality of PDP (Plasma Display Panel). For this reason, it makes efforts in searching defects more than 30 un as 100%. Now, the existing inspection is dependent upon naked eye or microscope in off-line PDP manufacturing process. In this study developed prototype inspection system of PDP 170 glass is based on line-scan mechanism. Developed system creates information that detects and sorts kinds of defect automatically. Designed inspection technology adopts multi-vision method by slip-beam formation for the minimum of inspection time and detection algorithm is embodied in detection ability of developed system. Designed algorithm had to make good use of kernel matrix that draws up an approach to geometry. A characteristic of defects, as pin hole, substance, protrusion, are extracted from blob analysis method. Defects, as open, short, spots and et al, are distinguished by line type inspection algorithm. In experiment, we could have ensured ability of inspection that can be detected with reliability of up to 95% in about 60 seconds.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.8-11
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• 2012

Automated Vision Inspection (AVI) systems automatically detect any defect feature in a surface image. The performance of the system can be measured under a special circumstances such as ultimate defect detection. In this situation, the defect signal level is similar to noise level and it becomes hard to make a solid decision with AVI systems. In this paper, we propose an effective preprocessing technique to enhance SNR (Signal to Noise Ratio). The method is motivated by some principles of HVS (Human Visual System) and RLC (Run Length Coding) techniques is used for this purpose. The proposed preprocessing technique enhances SNR under ultimate defect conditions and improves overall performance of AVI system.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.64-73
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• 1998

This paper is concerned with defects detection and evaluation of heat affected zone (HAZ) in austenitic stainless steel type 304 by ultrasonic wave and neural network. In experiment, the reflected ultrasonic defect signals from artificial defects (side hole, vertical hole, notch) of HAZ appears as beam distance of prove-defect, distance of probe-surface, depth of defect-surface on CRT. For defect classification simulation, neural network system was organized using total results of ultrasonic experiment. The organized neural network system was learned with the accuracy of 99%. Also it could be classified with the accuracy of 80% in side hole, and 100% in vertical hole, 90% in notch about ultrasonic pattern recognition. Simulation results of neural network agree fairly well with results of ultrasonic experiment. Thus were think that the constructed system (ultrasonic wave - neural network) in this work is useful for defects dection and classification such as holes and notches in HAZ of austenitic stainless steel 304.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.870-876
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• 2017

In this paper, we proposed the automatic vision inspection system using multi-layer perceptron to detect the defects occurred on rail surface. The proposed system consists of image acquisition part and analysis part. Rail surface image is acquired as equal interval using line scan camera and lighting. Mean filter and dynamic threshold is used to reduce noise and segment defect area. Various features to characterize the defects are extracted. And they are used to train and distinguish defects by MLP-classifier. The system is installed on HEMU-430X and applied to analyze the rail surface images acquired from Honam-line at high speed up to 300 km/h. Recognition rate is calculated through comparison with manual inspection results.

• Journal of Power System Engineering
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• v.14 no.3
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• pp.46-51
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• 2010

A torsional guided wave was applied to detect a defect in petrochemical pipes. Phase and group velocity dispersion curves for the longitudinal and torsional modes of the inspected pipe were presented for the theoretical analysis. It was found through mode shape analysis that there was mode conversion when torsional wave is incident at an asymmetric defect. An artificial notch was fabricated in the pipe and the detectability was examined from the distance 2m of the end of the pipe by using magnetostrictive sensors. The relativities between the amplitude of the reflected signal and the size of the defect was examined. It was shown that the T(0,1) mode could be used for the long range inspection for the petrochemical pipes.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.499-506
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• 2004

The copper foil surface inspection system is necessary for the factory automation and product quality. The developed system is composed of the high speed line scan camera, the image capture board and the processing computer. For the system resource utilization and real-time processing, multi-threaded architecture is introduced. There are one image capture thread, 2 or more defect detection threads, and one defect communication thread. To process the high-speed input image data, the I/O overlap is used through the double buffering. The defect is first detected by the predetermined threshold. To cope with the light irregularity, the compensation process is applied. After defect detection, defect type is classified with the defect width, eigenvalue ratio of the defect covariance matrix and gray level of defect. In experiment, for high-speed input image data, real-time processing is possible with multi -threaded architecture, and the 89.4% of the total 141 defects correctly classified.