• 한국공작기계학회논문집
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• 제12권6호
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• pp.56-63
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• 2003

In this study, an automatic ultrasonic testing system is used to detect the defects of the natural flaw test specimen and of the artificial flaw test specimen. We evaluate the detection performance of the acceptance standard for the natural flaw test specimen and of the acceptance standard for the artificial flaw test specimen. We also study the potential problems of those acceptance standards. The results indicate that the acceptance standard for the detection of defects in weldzone is good then the sensitivity correction is performed and that we must clearly specify special check points of the acceptance standard for the system in use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.245-265
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• 2022

In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAM achieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.

• 비파괴검사학회지
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• 제35권5호
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• pp.341-348
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• 2015

능동적 열화상 기법은 재료의 수동적 열적결함에 있어 기존의 적외선 열화상 기법에 비해 우수한 결함 검출능력을 보이는 것으로 알려져 있다. 적외선 비파괴 검사는 지금까지 다양한 검출 기법에 대한 발전이 이루어졌으나 신뢰성에는 다소 의문이 있다. 따라서 본 논문에서는 위상잠금 열화상기법을 적용하여 각각 다른 결함의 크기와 깊이의 인공결함을 갖는 SM45C 시험편을 가지고 제안된 기법을 검증하고, 불확도를 평가하여 위상잠금 열화상 기법을 이용한 결함의 크기측정에 대한 신뢰성을 검토하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.136-146
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• 2022

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.

• 한국공작기계학회논문집
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• 제15권2호
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• pp.87-92
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• 2006

This paper supplements shortcoming of radioactivity check by detecting defect of SWP weld zone using ultrasonic wave. Manufacture 2 stage robot detection systems that can follow weld bead of SWP by method to detect weld defects of SWP that shape of weld bead is complex for this as quantitative. Also, through signal processing ultrasonic wave defect signal system of GUI environment that can grasp easily existence availability of defect because do videotex compose. Ultrasonic wave signal of weld defects develops artificial intelligence style sightseeing system to enhance pattern recognition of weld defects and the classification rate using neural net. Classification of weld defects that do fan Planar defect and that do volume defect of by classify.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.444-449
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• 1996

The semiconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip affect on the functions of the semiconductors. The defects of the chip surface are cracks or voids. As general inspection method requires many inspection procedure, the inspection system which searches immediately and precisely the defects of the semiconductor chip surface is required. We suggest the detection algorithm for inspecting the surface defects of the semiconductor surface. The proposed algorithm first regards the semiconductor surface as random texture and point spread function, and secondly presents the character of texture by linear estimation theorem. This paper assumes that the gray level of each pixel of an image is estimated from a weighted sum of gray levels of its neighbor pixels by linear estimation theorem. The weight coefficients are determined so that the mean square error is minimized. The obtained estimation window(two-dimensional estimation window) characterizes the surface texture of semiconductor and is used to discriminate the defects of semiconductor surface.

• 전기학회논문지
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• 제67권5호
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• pp.669-674
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• 2018

Data augmentation is an efficient way to reduce overfitting on models and to improve a performance supplementing extra data for training. It is more important in deep learning based industrial machine vision. Because deep learning requires huge scale of learning data to learn a model, but acquisition of data can be limited in most of industrial applications. A very generic method for augmenting image data is to perform geometric transformations, such as cropping, rotating, translating and adjusting brightness of the image. The effectiveness of data augmentation in image classification has been reported, but it is rare in defect inspections. We explore and compare various basic augmenting operations for the metal surface defects. The experiments were executed for various types of defects and different CNN networks and analysed for performance improvements by the data augmentations.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.237-239
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• 2009

The purpose of this research is to propose a system to detect a strip defect on a iron plate using an image processing, one way of finding defects on an iron plate. An existing way of image processing is using a light source which release a light energy in a certain frequency and a light absorbing display which responds to the light source. This research attempts to detect defects by using the image processing which handles an illumination, without depending on characteristics of light frequency. One of the advantages of this method is that it makes up for the weakness of the existing method which was too difficult for users to notice a defect. Also this method makes it possible to realize a real-time monitoring on a plate of iron.

• 비파괴검사학회지
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• 제35권6호
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• pp.398-406
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• 2015

This paper explored the results of experimental investigation on carbon fiber reinforced polymer (CFRP) composite sample with thermal wave technique. The thermal wave technique combines the advantages of both conventional thermal wave measurement and thermography using a commercial Infrared camera. The sample comprises the artificial inclusions of foreign material to simulate defects of different shape and size at different depths. Lock-in thermography is employed for the detection of defects. The temperature field of the front surface of sample was observed and analysed at several excitation frequencies ranging from 0.562 Hz down to 0.032 Hz. Four-point methodology was applied to extract the amplitude and phase of thermal wave's harmonic component. The phase images are analyzed to find qualitative and quantitative information about the defects.

• Journal of Advanced Marine Engineering and Technology
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• 제23권4호
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• pp.473-479
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• 1999

The two-microphone sound-intensity technique has been used for the detection of defects in ra-ally loaded ball bearings. The difference in the sound-intensity levels measured for bearings with no defect and for those with intentionally introduced defects of different sizes n heir elements under various operating conditions of loads and speeds is demonstrated. The results show that of an inner-race or ball defect. It is difficult to detect defects at lower speeds. Sound-pressure measurements were also performed for comparison and it shown that the detectability of defects by sound-intensity measurements is better than that by sound-pressure measurements.