• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1044-1050
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• 2011

The quality of brazing joints is one of the most important factors that have an effect on the performance of the brazing joint-based heat exchangers with the growing use in industry recently. Therefore, it is necessary to inspect the brazing joints in order to guarantee the performance of the heat exchangers. This paper presents a non-destructive method to inspect the brazing joints of the heat exchangers using X-ray. Firstly, X-ray cross-sectional images of the brazing joints are obtained by using CT (Computerized Tomography) technology. Cross-sectional image from CT is more useful to detect the inner defects than the traditional transmitted X-ray image. Secondly, the acquired images are processed by an algorithm proposed for the defect detection of brazing joint. Finally, two types of brazing joint are examined in a series of experiments to detect the defects in brazing joints. The experimental results show that the proposed algorithm is effective for defect detection of the brazing joints in heat exchangers.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.487-493
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• 2004

In this Paper, we propose a lighting system for the stable detection of check defects of the CRT panel through the analysis of the lighting interaction between the lighting unit and the CRT panel. The check defect is very difficult to detect reliably because of its high sensitivity according to the direction of incident light. At first, we model the physical shape of check defects using SEM image. And then we apply physics based illumination model to investigate the optical characteristics of the check defect. Finally, we propose a lighting system for the stable detection of check defect. Experimental results show the feasibility of the proposed lighting system for check inspection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.687-701
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• 2020

In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.

• Proceedings of the KWS Conference
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• 2006.05a
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• pp.148-150
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• 2006

In this paper, the applicability of an UET(ultrasound excited thermography) for a defect detection of the welded receptacle is described. An UET(ultrasound excited thermography) is a defect-selective and fast imaging tool for damage detection. A high power ultrasound-excited vibration energy with pulse durations of 280ms is injected into the outer surface of the welded receptacle made of Al material. An ultrasound vibration energy sent into the welded receptacle propagate inside the sample until they are converted into the heat in the vicinity of the defect. The injection of the ultrasound excited vibration energy results in heat generation so that the defect is turned into a local thermal wave transmitter. Its local heat emission is monitored by the thermal infrared camera. And they are processed by the image recording system. Measurement was performed on aluminum receptacle welded by using Nd:YAG laser. The observed thermal image revealed two area of defects along the welded seam.

• The Journal of Information Systems
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• v.32 no.4
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• pp.149-162
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• 2023

Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

• Management & Information Systems Review
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• v.22
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• pp.211-229
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• 2007

The ISO/TS16949 APQP goal of defect prevention and decrease of spread waste, is the customer satisfaction which leads a continuous improvement and profit creation. The quality expense where the most is caused by but with increase of production initial quality problem occurrence is increasing to is actuality. Like this confirmation amendment. with the problem which is forecast in the place development at the initial stage which it does completeness it does not confront not to be able, production phase to be imminent, the problem accumulates and it talks the development shedding of which occurs. In opposition, prediction confrontation. is forecast in development early stage to and it is a structure which does not occur a problem to production early stage. Like this development is a possibility of accomplishing competitive company from production phase. Which attains an goal of, chance cause it leads a APQP activity (common cause) with special cause prevention & detection the connection characteristic of the focus technique against a interaction is important. And the customer requirement satisfaction and must convert a APQP goal of attainment at the key characteristics action step. (1) The Prevention - with Design FMEA application prevention of the present design management/detection, (2) the Detection (prevention/detection) - with Process FMEA application prevention of the present process control/detection, (3) Special Cause - statistical process control (SPC) 4M cause spread removal, (4) Common Cause - statistical process control (SPC) the nothing zero defect which leads the continuous improvement back of spread with application it will be able to attain with application.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.1
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• pp.34-39
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• 2013

In the advanced research deducted infrared thermography (IRT) test using 4 inch pipe with artificial wall-thinning defect to measure on the wall-thinned nuclear pipe components. This study conducted for defect detection condition of nuclear small-bore pipe research using deducted condition in the advanced research. Defect process is processed by change for defect length, circumferential direction angle, wall-thinning depth. In the used equipment IR camera and two halogen lamps, whose full power capacitany is 1 kW, halogen lamps and Target pipe experiment performed to the distance of the changed 1 m, 1.5 m, 2 m. To analysis of the experimental results ensure for the temperature distribution data, by this data measure for defect length. artificial defect of 4 inch pipe is high reliability in the 2 m, but small-bore pipe is in the 1.5 m from the defect clearly was detected.