• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.600-602
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• 2019

Metals are used in a variety of industrial sites and daily life. Metals are often used in machinery, automobile parts, wires, and robots. Metal causes small damage to the metal surface for a variety of reasons, such as the processing process and the user's operating environment. In this paper, phase detection for the inspection of defects in metals has been implemented. Using the electrical conductivity of metal, a circuit whose phase varies with the depth and size of the defect, and with the changed phase, the depth and size of the defect can be estimated.

• Journal of Welding and Joining
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• v.32 no.5
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• pp.50-57
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• 2014

Friction Stir Welding (FSW) is a complex process with several mutually interdependent parameters. A slight difference from known settings may lead to imperfections in the stirred zone. These inhomogeneities affect on the mechanical properties of the FSWed joints. In order to prevent the failure of the welded joint it is necessary to detect the most critical defects non-destructive. Especially critical defects are wormhole and lack of penetration (LOP), because of the difficulty of detection. Online thermography is used process-accompanying for defect detecting. A thermographic camera with a fixed position relating to the welding tool measures the heating-up and the cool down of the welding process. Lap joints with sound weld seam surfaces are manufactured and monitored. Different methods of evaluation of heat distribution and intensity profiles are introduced. It can be demonstrated, that it is possible to detect wormhole and lack of penetration as well as surface defects by analyzing the welding and the cooling process of friction stir welding by passive online thermography measurement. Effects of these defects on mechanical properties are shown by tensile testing.

• Journal of Biosystems Engineering
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• v.20 no.4
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• pp.368-375
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• 1995

A computer vision system was built to generate images of a stationary egg. This system includes a. CCD camera, a frame grabber, and an incandescent back lighting system An image processing algorithm was developed to accurately detect surface holes and surface cracks on eggs. With 20W of incandescent back light, the detection rate was shown to be the highest. The Sobel operator was found to be the best among various enhancing filters examined. The threshold value to distinguish between the crack and the translucent spots was found to be linear with the average gray level of a whole egg image. Those values of both gray level and area were used as criteria to detect holes in egg and those values of both area and roundness were used to detect cracks in egg. For a sample of 300 eggs, this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. On the average, it took 59.5 seconds to analyze an egg image and determine whether or not it was defected.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.304-311
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• 2014

In this paper, we propose a ultra sound inspection technique for automatic defect detection and classification in aircraft composite materials. Using local maximum values of ultra sound wave, we choose peak values for defect detection. Distance data among peak values are used to construct histogram and to determine surface and back-wall echo from the floor of composite materials. C-scan image is then composed through this method. A threshold value is determined by average and variance of the peak values, and defects are detected by the values. PCA(principal component analysis) and QDA(quadratic discriminant analysis) are carried out to classify the types of defects. In PCA, 512 dimensional data are converted into 30 PCs(Principal Components), which is 99% of total variances. Computational cost and misclassification rate are reduced by limiting the number of PCs. A decision boundary equation is obtained by QDA, and defects are classified by the equation. Experimental result shows that our proposed method is able to detect and classify the defects automatically.

• Clean Technology
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• v.23 no.1
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• pp.90-94
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• 2017

These days much efforts are being dedicated to wind power as a potential source of renewable energy. To maintain effective and uniform generation of energy, defect preservation of turbine blade is essential because it directly takes effects on the efficiency of power generation. For the effective maintenance, early measurements of blade defects are very important. However, current technologies such as ultrasonic waves and thermal imaging inspection methods are not suitable because of long inspection time and non-real time inspection. To supplement the problems, the study introduced a method for real time defect monitoring of a blade surface based on surface wave technology. We examined the effect of various parameters such as micro-cracks and peelings on the propagation of surface wave.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.834-841
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• 2022

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.6
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• pp.637-642
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• 2002

An arc-shaped line array slit has been used for the laser generation of focused Lamb waves. The spatially expanded Nd:YAG pulse laser was illuminated through the arc-shaped line array slit on the surface of a sample plate to generate the Lamb waves of the same pattern as the slit. Then the generated Lamb waves were focused at the focal point of which distance from the slit position is dependent on the curvature of slit arc. The proposed method showed better spatial resolution than the conventional linear array slit in the detection of laser machined linear defect and drill machined circular defect on aluminum plates of 2mm thickness. Using the focused waves, we could detect the linear defect and the circular defect with the improvement of spatial resolution. The method can also be combined with the scanning mechanism to get an image just like by the scanning acoustic microscope(SAM).

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.505-510
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• 2024

Since rolling contact fatigue cracks can always occur on the rail surface, which is the contact surface between wheels and rails, railway rails require thorough inspection and diagnosis to thoroughly inspect the condition of the cracks and prevent breakage. Recent detailed guidelines on the performance evaluation of track facilities present the requirements for methods and procedures for track performance evaluation. However, diagnosing and grading rail surface damage mainly relies on external inspection (visual inspection), which inevitably relies on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we conducted a deep learning model study for rail surface defect detection using Fast R-CNN. After building a dataset of rail surface defect images, the model was tested. The performance evaluation results of the deep learning model showed that mAP was 94.9%. Because Fast R-CNN has a high crack detection effect, it is believed that using this model can efficiently identify rail surface defects.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1271-1283
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• 2007

To detect defect of FPD(flat panel displays) is very difficult due to uneven illumination on FPD panel image. This paper presents a method to detect various types of defects using the approximated image of the uneven illumination by B-spline. To construct a approximated surface, corresponding to uneven illumination background intensity, while reducing random noises and small defect signal, only the lowest smooth subband is used by wavelet decomposition, resulting in reducing the computation time of taking B-spline approximation and enhancing detection accuracy. The approximated image in lowest LL subband is expanded as the same size as original one by wavelet reconstruction, and the difference between original image and reconstructed one becomes a flat image of compensating the uneven illumination background. A simple binary thresholding is then used to separate the defective regions from the subtracted image. Finally, blob analysis as post-processing is carried out to get rid of false defects. For applying in-line system, the wavelet transform by lifting based fast algorithm is implemented to deal with a huge size data such as film and the processing time is highly reduced.

• Journal of Surface Science and Engineering
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• v.55 no.6
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• pp.398-402
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• 2022

Vacuum Insulation Panel (VIP) is an high energy efficient insulation system that facilitate slim but high insulation performance, based on based on a porous core material evacuated and encapsulated in a multi-barrier envelope. Although VIP has been on the market for decades now, it wasn't until recently that efforts have been initiated to propose a standard on aging testing. One of the issues regarding VIP is its durability and aging due to pressure and moisture dependent increase of the initial low thermal conductivity with time. It is hard to visually determine at an early stage. Recently, a method of analyzing the damage on the a material surface by applying image processing technology has been widely used. These techniques provide fast and accurate data with a non-destructive way. In this study, the surface VIP images were analyzed using the Harris corner detection algorithm. As a result, 171,333 corner points in the normal packaging were detected, whereas 32,895 of the defective packaging, which were less than the normal packaging. were detected. These results are considered to provide meaningful information for the determination of VIP condition.