• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.8
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• pp.1793-1798
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• 2005

We model the nucleation and motion of defects in the liquid crystal display device with inhomogeneous surface by using fast Q-tensor method, which can calculate scalar order parameter S and nucleation of the defect in the liquid crystal director field. In order to model the defect, homeotropic aligned liquid crystal cell with step inhomogeneous electrode which has a height of $1{\mu}m$ is used. From the simulation, we can observe the nucleation and line of the defect from surface inhomogeneity and the experiment is performed for confirmation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.831-832
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• 2010

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.19-26
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• 2020

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.273-275
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• 2005

Active thermography is being used since several years for remote non-destructive testing. It provides thermal images for remote detection and imaging of damages. Also, it is based on propagation and reflection of thermal waves which are launched from the surface into the inspected component by absorption of modulated radiation. For energy deposition, it use external heat sources (e.g., halogen lamp or convective heating) or internal heat generation (e.g., microwaves, eddy current, or elastic wave). Among the external heat sources, the ultrasound is generally used for energy deposition because of defect selective heating up. The heat source generating a thermal wave is provided by the defect itself due to the attenuation of amplitude modulated ultrasound. A defect causes locally enhanced losses and consequently selective heating up. Therefore amplitude modulation of the injected ultrasonic wave turns a defect into a thermal wave transmitter whose signal is detected at the surface by thermal infrared camera. This way ultrasound thermography(UT) allows for selective defect detection which enhances the probability of defect detection in the presence of complicated intact structures. In this paper the applicability of UT for fast defect detection is described. Examples are presented showing the detection of defects in PCB material. Measurements were performed on various kinds of typical defects in PCB materials (both Cu metal and non-metal epoxy). The obtained thermal image reveals area of defect in row of thick epoxy material and PCB.

• Transactions of Materials Processing
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• v.31 no.4
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• Bulletin of the Society of Naval Architects of Korea
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• v.21 no.1
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• pp.35-42
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• 1984

In the present study, rotating bending fatigue tests have been carried out in three kinds of carbon steel specimens; an annealed low carbon steel, an annealed high carbon steel and quenched-tempered high carbon steel; with a small artificial surface defect that might exist in real structures. Fatigue crack lengths have been observed by a method of replication in order to investigate the growth characteristic of fatigue crack in the viewpoints of strength of materials and fracture mechanics. The main results obtained are as follows: 1) The effect of a small surface defect upon the reduction of fatigue limit is considerably large, and the rate of fatigue limit reduction grows in the following order; annealed low carbon steel(mild steel), annealed high carbon steel, quenched-tempered high carbon steel. 2) When the growth rate of surface crack length(2a) was investigated in the viewpoints of fracture mechanics based upon $ ${\Delta}K_{\varepsilon}$, the dependence of stress level and of surface defect size disappear, and there exists a linear relationships between d(2a)/dN and ${\Delta}K_{{\varepsilon}t},\;\Delta_{{\varepsilon}t}\sqrt{{\pi}a}$, on log. plot, i.e, $d(2a)/dN={C{\cdot}{\Delta}K_{\varepsilon}}^3_t$, where ${\Delta}_{{\varepsilon}t}\sqrt{{\pi}a}$ a is the cyclic total strain intensity factor range.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.5
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• pp.1202-1210
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• 1995

Aiming at nondestructive evaluation of defect with high accuracy and resolution, ICFPD(Induced Current Focusing Potential Drop) technique was newly developed. This technique can be applied for locating and sizing of defects in components with not only simple shape such as plain surface but also more complex shape and geometry such as curved surface and dissimilar joing. This paper describes the principle of ICFPD technique and also the results of 2-dimensional surface crack in ferromagnetic metal(A508 Cl. III steel) and paramagnetic metal (pure aluminum and stainless 304 steel) measured by this technique. Results are that surface defects in each specimen are detected with the difference of potential drop, and potential drops are distributed a similar shape for each metal and each depth. The normalized potential drop ( $V_{\delta}$2/$^{t}$ / $V_{{\delta} 2}$$^{-1}$) max. in the vicinity of defect is varied with the depth of defect. Therefore, ICFPD technique can be used for the evaluation of defect not only in ferromagnetic metal but also in paramagnetic steel..

• Proceedings of the KWS Conference
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• 1995.10a
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• pp.214-217
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• 1995

In order to investigate characteristics of surface fatigue crack propagation from a pit shaped surface defect which frequently exists near weld joints, SS400 steel with thickness of 12mm, which generally used for structure members, was submerged-arc welded with butt type and machined for both surface. The weld joints were devided into 5 regions, weld metal, boundary between heat affected zone (HAZ), HAZ, boundary between HAZ and base metal, and base metal. Specimens from each region were machined for a pit shaped initial surface defect with aspect ratio of 2. characteristics of surface fatigue crack por pagation from the defect under the same loading condition were compared and discussed.

• Smart Structures and Systems
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• v.25 no.2
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• pp.255-264
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• 2020

This paper deals with a defect classification technique that considers the structural characteristics of a refrigerant compressor. First, the pressure pulsation of the refrigerant flowing in the suction pipe of a normal compressor was measured at the same time as the acceleration of the shell surface, and then the transfer function between the two signals was estimated. Next, the frequency-weighted acceleration signals of the defect classification target compressors were generated using the estimated transfer function. The estimation of the variance of the transfer function is presented to formulate the frequency-weighted acceleration signals. The estimated frequency-weighted accelerations were applied to defect classification using frequency-domain features. Experiments were performed using commercial compressors to verify the technique. The results confirmed that it is possible to perform an effective defect classification of the refrigerant compressor by the shell surface acceleration of the compressor. The proposed method could make it possible to improve the total inspection performance for compressors in a mass-production line.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.668-670
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• 1999

We have developed a automatic surface inspection system for cold Rolled strips in steel making process for several years. We have experienced the various kinds of surface inspection systems, including linear CCD camera type and the laser type inspection system which was installed in cold rolled strips production lines. But, we did not satisfied with these inspection systems owing to insufficient detection and classification rate, real time processing performance and limited line speed of real production lines. In order to increase detection and computing power, we have used the Dark Field illumination with Infra_Red LED, Bright Field illumination with Xenon Lamp, Parallel Computing Processor with Area typed CCD camera and full software based image processing technique for the ease up_grading and maintenance. In this paper, we introduced the automatic inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms. As a result of experiment, under the situation of the high speed processed line(max 1000 meter per minute) defect detection is above 90% for all occurred defects in real line, defect name classification rate is about 80% for most frequently occurred 8 defect, and defect grade classification rate is 84% for name classified defect.