• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.58-67
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• 2000

Electron gun heaters are used to heat a cathode in video(TV) monitors. Major defects of the electron gun heaters include dimensional inaccuracy and pollution with dirty materials. In this paper, to save the labor and time being taken to inspect the heaters, a machine vision system is considered. For the system, a new algorithm is developed to measure the 9 different dimensions of each heater and to detect polluted defects. The algorithm consists of three stages. In the first stage, the center of the heater image is obtained and then its boundary detection is performed. For the efficient boundary detection, a mask called the sum mask is used. In the second stage of the algorithm, a set of fiducial points are determined on the boundary image. Finally, using the fiducial points specified dimensions are measured and the amount of polluted area is computed in the third stage. The performance of the algorithm is evaluated for a set of real specimens. The results indicate that measurements obtained by the algorithm satisfy the tolerance limits fur most of the dimensions and the algorithm detects the polluted defects successfully.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.89-93
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• 2002

This paper introduces a vision-based on-line fabric inspection methodology of woven textile fabrics. Current procedure for determination of fabric defects in the textile industry is performed by human in the off-line stage. The advantage of the on-line inspection system is not only defect detection and identification, but also 벼ality improvement by a feedback control loop to adjust set-points. The proposed inspection system consists of hardware and software components. The hardware components consist of CCD array cameras, a frame grabber and appropriate illumination. The software routines capitalize upon vertical and horizontal scanning algorithms characteristic of a particular deflect. The signal to noise ratio (SNR) calculation based on the results of the wavelet transform is performed to measure any deflects. The defect declaration is carried out employing SNR and scanning methods. Test results from different types of defect and different style of fabric demonstrate the effectiveness of the proposed inspection system.

• Journal of Power System Engineering
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• v.8 no.3
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• pp.52-57
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• 2004

A new non-destructive inspection technique has been developed. One characteristic of the technique is that defects are visualized by laser ray. Magnetic domains and domain walls of a magneto-optical sensor(MO sensor) are varied by the magnetic flux leaked by defects, and the variations are observed by the reflected light of the laser ray. The information of defect can remotely be inspected by this technique in a real time. This paper describes the results estimated on the 2-dimensional surface defects and opposite-side defects in a ferromagnetic material and the natural surface defect in a clutch disk wheel. The light region of a visible image and the magnitude of a reflected light increases as the input current of the magnetizer increases. The natural surface defect, that has not the width of crack's open mouth, can be also visualized like as 2-dimensional artificial defects.

• Journal of Magnetics
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• v.18 no.3
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• pp.357-360
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• 2013

A modified SQI method using magnetic leakage flux (MFL) signal for underground gas pipelines' defect detection and characterization is presented in this paper. Raw signals gathered using MFL signals include many unexpected noises and high frequency signals, uneven background signals, signals caused by real defects, etc. The MFL signals of defect free pipelines primarily consist of two kinds of signals, uneven low frequency signals and uncertain high frequency noises. Leakage flux signals caused by defects are added to the case of pipelines having defects. Even though the SQI (Self Quotient Image) is a useful tool to gradually remove the varying backgrounds as well as to characterize the defects, it uses the division and floating point operations. A modified SQI having low computational complexity without time-consuming division operations is presented in this paper. By using defects carved in real pipelines in the pipeline simulation facility (PSF) and real MFL data, the performance of the proposed method is compared with that of the original SQI.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.2
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• pp.96-103
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• 2005

The early detection of the bearing defects in rotating machinery is very important since the critical failure of bearing causes a machinery shutdown. However it is not easy to detect the vibration signal caused by the initial defects of bearing because of the high level of random noise. A signal processing technique, called the adaptive line enhancer(ALE) as one of adaptive filter, is used in this study. This technique is to eliminate random noise with little a prior knowledge of the noise and signal characteristics. Also we propose the optimal methods fir selecting the three main ALE parameters such as correlation length filter order and adaptation constant. Vibration signals f3r three abnormal bearings, including inner and outer raceways and ball defects, were acquired by Anderon(angular derivative of radius on) meter. The experimental results showed that ALE is very useful f3r detecting the bearing defective signals masked by random noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.944-951
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• 2009

In this paper, we use image processing algorithms to detect the defects existed on a welding area automatically. It is difficult to detect the welding defects because it is sensitive to lights and has irregular patterns. For this reason, images are captured with 2 kinds of illumination condition, and are processed by 2 different algorithms for each image. The first algorithm separates some ROI's from the captured image and compares the similarity of intensity between each divided region. The second algorithm extracts boundary information from the processed image by the first algorithm, and calculates the length of boundary, curvature and base line area based on boundary information. The proposed method showed high performance in detection and classification of defects.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.123-128
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• 2016

In manufacturing processing of a secondary battery, the visual inspection system is studied and developed to check the surface defects of the electrode plates. It consists of two parts, one is the hardware control and the other software implementation. The former is made up to the system configuration and the design of the optical system, the illuminations and the controllers. The latter is the detection algorithms of the surface defects. This system achieves the quality improvement of the electrode process and the price competitiveness. By using the proposed defects detection algorithms this system demonstrates the high reliability of spot, line, manhole, extraneous substance, scratch, and crater defect of a electrode plate surface.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.24-29
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• 1996

The seniconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip effect on the functions of the semiconductors. The defects of the chip surface is crack or void. Because general inspection method requires many inspection processes, the inspection system which searches immediately and preciselythe defects of the semiconductor chip surface. We propose the inspection method by using the computer vision system. This study presents an image processing algorithm for inspecting the surface defects(crack, void)of the semiconductor test samples. The proposed image processing algorithm aims to reduce inspection time, and to analyze those experienced operator. This paper regards the chip surface as random texture, and deals with the image modeling of randon texture image for searching the surface defects. For texture modeling, we consider the relation of a pixel and neighborhood pixels as noncasul model and extract the statistical characteristics from the radom texture field by using the 2D AR model(Aut oregressive). This paper regards on image as the output of linear system, and considers the fidelity or intelligibility criteria for measuring the quality of an image or the performance of the processing techinque. This study utilizes the variance of prediction error which is computed by substituting the gary level of pixel of another texture field into the two dimensional AR(autoregressive model)model fitted to the texture field, estimate the parameter us-ing the PAA(parameter adaptation algorithm) and design the defect detection filter. Later, we next try to study the defect detection search algorithm.

• Journal of Magnetics
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• v.22 no.1
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.90-93
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• 2000

In this study, the rise throughput and the stability in fabrication of device can be obtained by applying of CMP process to STI structure in 0.18um semiconductor device. To employ in STI CMP, the reverse moat process has been added thus the process became complex and the defects were seriously increased. Removal rates of each thin films in STI CMP was not equal hence the devices must to be effected, that is, the damage was occured in the device dimension in the case of excessive CMP process and the nitride film was remained on the device dimension in the case of insufficient CMP process than these defects affect the device characteristics. To resolve these problems, the development of slurry for CMP with high removal rate and high selectivity between each thin films was studied then it can be prevent the reasons of many defects by reasons of many defects by simplification of process that directly apply CMP process to STI structure without the reverse moat pattern process.