• Journal of IKEEE
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• v.25 no.4
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• pp.689-696
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• 2021

In this paper, in order to apply Prognostics and Health Management(PHM) to an electronic system or circuit, a circuit capable of detecting and predicting defect characteristics inside the system or circuit is implemented, and the results are described. In the previous study, we demonstrated that the frequency of the amplitude of S-parameter changed as the circuit defect progressed. These characteristics were measured by network analyser. but in this study, even if the same defect detection method is used, a circuit is proposed to check the progress of the defect, the remaining time, and the occurrence of the defect without large measurement devices. The circuit is designed to detect the change in impedance that generates changes of S-parameter, and it is verified through simulation using the measurement results of Bond-wires.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.337-343
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• 1996

In this paper, we investigate the defect detection by making use of pre-made reference image data and classify the defects by using the artificial neural network. The approach is composed of three main parts. The first step consists of a proper generation of two reference image data by using a low level morphological technique. The second step proceeds by performing three times logical bit operations between two ready-made reference images and just captured image to be tested. This results in defects image only. In the third step, by extracting four features from each detected defect, followed by assigning them into the input nodes of an already trained artificial neural network we can obtain a defect class corresponding to the features. All of the image data are formed in a bit level for the reduction of data size as well as time saving. Experimental results show that proposed algorithms are found to be effective for flexible defect detection, robust classification, and high speed process by adopting a simple logic operation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.123-130
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• 2014

In TFT-LCD panel images, defects are typically detected by using a large difference in the brightness compared to the background. In this paper, we propose a sequential defect detection algorithm according to defect possibility caused by difference of brightness. By using this method, pixels with high defect probabilities are preferentially detected and defects with a large brightness difference are accurately detected. Also, limited defects with a small brightness difference is detected more reliably, eventually minimizing the degree of over-detection. We have experimentally confirmed that our proposed method showed an excellent detection result for detecting limited defects as well as defects with a large brightness difference.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.107-117
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• 2016

STD method using a mean and standard deviation is widely used in various inspection systems. The result of detection using the STD method is very dependent on the threshold value. This paper proposes an adaptive defect detection algorithm to with a precise detection of an ultimate defect. The proposed method is determined threshold value adaptively using a skewness that indicates a similarity of intensity and normal distribution of image. In the experiment, we used a various TFT-LCD images for a quantitative evaluation of defect detection performance evaluation result to prove the performance of the proposed algorithm.

• 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.

• Steel and Composite Structures
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• v.19 no.1
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• pp.131-152
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• 2015

Easy detection and evaluation of defect in the tube structure is a continuous problem and remains a significant demand in tube inspection technologies. This study is aimed to automate defect detection using the pattern recognition approach based on the classification of high frequency stress wave signals. The stress wave signals from vibrational impact excitation on several tube conditions were captured to identify the defect in ASTM A179 seamless steel tubes. The variation in stress wave propagation was captured by a high frequency sensor. Stress wave signals from four tubes with artificial defects of different depths and one reference tube were classified using the autoregressive (AR) algorithm. The results were demonstrated using a dendrogram. The preliminary research revealed the natural arrangement of stress wave signals were grouped into two clusters. The stress wave signals from the healthy tube were grouped together in one cluster and the signals from the defective tubes were classified in another cluster. This approach was effective in separating different stress wave signals and allowed quicker and easier defect identification and interpretation in steel tubes.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.31-35
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• 2010

This paper presents the development of automatic defect detection program using 3D computed tomography image of small solid rocker motor. We applied the neighbor pixel comparison algorithm with beam hardening correction for the recognition of defect. We made the artificial defect specimen in order to decide a standard CT value of defect. The program was tested with 150 small solid rocket motors and it could detect the disbond, crack, foreign material and void. The program showed more reliable and faster results than human inspector's interpretation.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.577-586
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• 2022

Detection and classification of steel surface defects are critical for product quality control in the steel industry. However, due to its low accuracy and slow speed, the traditional approach cannot be effectively used in a production line. The current, widely used algorithm (based on deep learning) has an accuracy problem, and there are still rooms for development. This paper proposes a method of steel surface defect detection combining EfficientNetV2 for image classification and YOLOv5 as an object detector. Shorter training time and high accuracy are advantages of this model. Firstly, the image input into EfficientNetV2 model classifies defect classes and predicts probability of having defects. If the probability of having a defect is less than 0.25, the algorithm directly recognizes that the sample has no defects. Otherwise, the samples are further input into YOLOv5 to accomplish the defect detection process on the metal surface. Experiments show that proposed model has good performance on the NEU dataset with an accuracy of 98.3%. Simultaneously, the average training speed is shorter than other models.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.513-520
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• 2020

Recently, the development of a vision inspection system using machine learning has become more active. This study seeks to develop a defect inspection model using machine learning. Defect detection problems for images correspond to classification problems, which are the method of supervised learning in machine learning. In this study, defect detection models are developed based on algorithms that automatically extract features and algorithms that do not extract features. One-dimensional CNN and two-dimensional CNN are used as algorithms for automatic extraction of features, and MLP and SVM are used as algorithms for non-extracting features. A defect detection model is developed based on four models and their accuracy and AUC compare based on AUC. Although image classification is common in the development of models using CNN, high accuracy and AUC is achieved when developing SVM models by converting pixels from images into RGB values in this study.