• Journal of Welding and Joining
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• v.16 no.3
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• pp.64-73
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• 1998

This paper is concerned with defects detection and evaluation of heat affected zone (HAZ) in austenitic stainless steel type 304 by ultrasonic wave and neural network. In experiment, the reflected ultrasonic defect signals from artificial defects (side hole, vertical hole, notch) of HAZ appears as beam distance of prove-defect, distance of probe-surface, depth of defect-surface on CRT. For defect classification simulation, neural network system was organized using total results of ultrasonic experiment. The organized neural network system was learned with the accuracy of 99%. Also it could be classified with the accuracy of 80% in side hole, and 100% in vertical hole, 90% in notch about ultrasonic pattern recognition. Simulation results of neural network agree fairly well with results of ultrasonic experiment. Thus were think that the constructed system (ultrasonic wave - neural network) in this work is useful for defects dection and classification such as holes and notches in HAZ of austenitic stainless steel 304.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1240-1242
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• 1996

In a still mill, the effective surface defect inspection algorithm is necessary. For this purpose, this paper proposed the preprocessing algorithm for surface defect inspection of cold mill strip. This consists of live steps. They are edge detection, binarizing, noise deletion, combining of fragmented defect and selecting the largest defect. Especially, binarizing is a critical problem. Bemuse the performance of the preprocessing is largely depend on the binarized image. So, we develope the adaptive thresholding method, which is multilevel thresholding. The thresholding value is varied according to the mean graylevel value of each test image. To investigate the performance of the proposed algorithm, we classified the detected defect using neural network. The test image is 20 defect images captured at German Sick Co. This algorithm is proved to have good property in cold mill strip surface inspection.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.166-173
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• 2014

Purpose: In this study, we focused on the image processing method to determine the external quality of Fuji apples by identifying surface defects such as scabs and bruises. Method: A CCD camera was used to capture filter images with 24 different wavelengths ranging between 530 nm and 1050 nm. Image subtraction and division operations were performed to distinguish the defect area from the normal areas including calyx, stem, and glaring on the apple surface image. All threshold values of the image were examined to reveal the defect area of pretreated filter images. Results: The developed operation methods were [image (720 nm) - image (900 nm)]/image (700 nm) for bruise detection and [image (740 nm) - image (900 nm)]/image (590 nm) for scab detection, which revealed 81% and 90% recognition ratios, respectively. Conclusions: Our results showed several optimal wavelengths and image processing methods to detect Fuji apple surface defects such as bruises and scabs.

• Journal of the Korean Institute of Navigation
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• v.22 no.4
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• pp.59-67
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• 1998

A quick and automatic detection with no harm to the goods is very important task for improving quality control, process control and labour reduction. In real fields of industry, defect detection is mostly accomplished by skillful workers. A narrow band eliminating spatial filter having characteristics of removing the specified spatial frequency is developed by the author, and it is proved that the filter has an excellent ability for on-line and real time detection of surface defect. By the way,. this spatial filter shows a ripple phenominum in filtering characteristics. So, it is necessary to remove the ripple component for the improvement of filter gain, moreover efficiency of defect detection. The spatial filtering method has a remarkable feature which means that it is able to set up weighting function for its own sake, and which can to obtain the best signal relating to the purpose of the measurement. Hence, having an eye on such feature, theoretical analysis is carried out at first for optimal design of narrow band eliminating spatial filter, and secondly, on the basis of above results spatial filter is manufactured, and finally advanced effectiveness of spatial filter is evaluated experimentally.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1060-1066
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• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material efficiently. In this paper a detection of the welding defect of thin SUS 304 plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (20kHz) ultrasonic transducer was used to infuse the welded thin SUS 304 plates with a short pulse of sound for 280ms. The ultrasonic source has a maximum power of 2kW. The surface temperature of the area under inspection is imaged by a thermal infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the defect tip and heated up highly, are observed. From the sequence of the thermosonic images, the location of defective or inhomogeneous regions in the welded thin SUS 304 plates can be detected easily.

• 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.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1940-1945
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• 2007

The wheelsets are one of most important component: damages in wheel tread and press fitted axle are a significant cost for railway industry. Since failure in railway wheelset can cause a disaster, regular inspection of defects in wheels and axles are mandatory. Ultrasonic testing, acoustic emission and eddy current testing method and so on regularly check railway wheelset in service. However, it is difficult to use this method because of its high viscosity and because its sensitivity is affected by temperature. Also, due to noise echoes it is difficult to detect defects initiation clearly with ultrasonic testing. It is necessary to develop a non-destructive technique that is superior to conventional NDT techniques in order to ensure the safety of railway wheelset. In the present paper, the new NDT technique is applied to the detection of surface defects for railway wheelset. To detect the defects for railway wheelset, the sensor for defect detection is optimized and the tests are carried out with respect to surface and internal defects each other. The results show that the surface crack depth of 1.5 mm in press fitted axle and internal crack in wheel could be detected by using the new method. The ICFPD method is useful to detect the defect that initiated in the tread of railway wheelset.

• Journal of Welding and Joining
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• v.13 no.2
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• pp.42-52
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• 1995

In the evaluation of aging degradation on the structural materials based on the fracture mechanics, the detection and size prediction of defect are very important. Aiming at nondestructive detection and size prediction ol defect with high accuracy and resolution, therefore, an lnduced Current Focusing Potential Drop(ICFPD) technique has been developed. The principle of this technique is to induce a focusing current at an exploratory region by an induction wire flowing an alternating current(AC) that is a constant ampere and frequency. Defects are assessed with the potential drops that are measured the induced current on the surface of metallic material by the potential pick-up pins. In this study, the lCFPD technique was applied for evaluating the location and size of the surface crack and blind crack made in plate specimens, and also for detecting the defects existing in valve, a field component, that were developed by SCC etc. during the service. The results of this present study show that surface crack and blind crack are able to defect with potential drop. these cracks are distinguished with the distribution of potential drop, and the crack depths can be estimated with each normalized potential drop that are parameters estimating the depth of each type crack. In the field component, the defects estimated by experiment result correspond with those in the cutting face of the measuring point within a higher sensitivity.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.