• Journal of the Korean Society for Precision Engineering
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• v.32 no.11
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• pp.989-995
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• 2015

In the literature, various stochastic anomaly detection methods, such as limit checking and PCA-based approaches, have been applied to weld defect detection. However, it is still a challenge to identify meaningful defect patterns from very limited sensor signals of laser welding, characterized by intermittent, discontinuous, very short, and non-stationary random signals. In order to effectively analyze the physical characteristics of laser weld signals: plasma intensity, weld pool temperature, and back reflection, we first transform the raw data of laser weld signals into the form of event logs. This is done by multidimensional discretization and event-codification, after which the event logs are decoded to extract weld defect patterns by $Na{\ddot{i}}ve$ Bayes classifier. The performance of the proposed method is examined in comparison with the commercial solution of PRECITEC's LWM$^{TM}$ and the most recent PCA-based detection method. The results show higher performance of the proposed method in terms of sensitivity (1.00) and specificity (0.98).

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.317-321
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• 2008

Piping in the Nuclear Power plants (NPP) are mostly consisted of carbon steel pipe. The wall thinning defect is mainly occurred by the affect of the flow accelerated corrosion (FAC) of fluid which flows in carbon steel pipes. This type of defect becomes the cause of damage or destruction of piping. Therefore, it is very important to measure defect which is existed not only on the welding partbut also on the whole field of pipe. Over the years, Infrared thermography (IRT) has been used as a non destructive testing methods of the various kinds of materials. This technique has many merits and applied to the industrial field but has limitation to the materials. Therefore, this method was combined with lock-in technique. So IRT detection resolution has been progressively improved using lock-in technique. In this paper, the quantitative analysis results of the location and the size of wall thinning defect that is artificially processed inside the carbon steel pipe by using IRT are obtained.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.43-51
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• 2014

TFT-LCD image includes a defect of various properties. TFT-LCD image have a recognizable defects in the human inspector. On the other hand, it is difficult to detect defects that difference between the background and defect is very low. In this paper, we proposed sequentially detect algorithm from pixels included in the defect region to limited defects. And blob analysis methods using the blob size and gray difference are applied to the defect candidate image. Finally, we detect an accurate defect blob to distinguish the noise. The experimental results show that the proposed method finds the various defects reliably.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.270-279
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• 2003

Parametric studies have been conducted into the variability of the factors affecting the ultrasonic testing applied to weldments. The influence of ultrasonic equipment, transducer parameters, test technique, job parameters, defect type and characteristics on reliability far defect detection and sizing was investigated by experimentation. The investigation was able to build up substantial bank of information on the reliability of manual ultrasonic method for testing weldments. The major findings of the study separate into two parts, one dealing with correlation between ultrasonic techniques, equipment and defect parameters and inspection performance effectiveness and other with human factors. Defect detection abilities are dependent on the training, experience and proficiency of the UT operators, the equipment used, the effectiveness of procedures and techniques.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.55-61
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• 2021

In this paper, we proposed and implemented defect detection methods of polarized film with half-wave phase retardation plates. We investigated the principles of phase retardation compensation and optical principle of half-wave phase retardation plates. We analyzed of samples of polarized film with half-wave phase retardation plates. The optical defect detection methods are proposed and the performance is validated with experiments.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1973-1979
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• 2000

Driving the need for machine vision system is growing consumer demand for quality and defect-free products. Especially it is the most important in tarpaulin's manufacturing process achieves automatically by machine vision instead of by man vision. In this paper pinholes detection is performed by using morphology algorithms. Top hat transform is one of morphology applications. This transform take high performance of defect detection in the case that unexpected changes occur in some non-uniform background. For pinholes defect, automatic visual inspection system has been developed, which was composed by a line-scan camera, illumination, a frame grabber, a motor driver and control units. This system has excellent capacity to defect pinholes to the 0.1 mm by 0.5 mm in size and to work in moving objects by maximum 20 m/min in speed.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1509-1511
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• 2015

Defect detection is one of the most challenging problems in industrial quality control. In this study we developed a vision-based defect detection system for wafer production. To achieve high-accuracy detection, Otsu method was improved so that it can handle both unimodal and bimodal distributions. After thresholding, detected defect regions in the wafer are classified and grouped into user-defined defect categories. The experimental result has proved the efficiency of our system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.709-710
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• 2008

• 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.