• Journal of Welding and Joining
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• 제13권4호
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• pp.132-146
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• 1995

Flaw classification(determination of the flaw type) and flaw sizing (prediction of the flaw shape, orientation and sizing parameters) are very important issues in ultrasonic nondestructive evaluation of weldments. In this work, new techniques for both classification and sizing of flaws in weldments are described together with extensive review of previous works on both topics. In the area of flaw classification, a methodology is developed which can solve classification problems using probabilistic neural networks, and in the area of flaw sizing, a time-of-flight equivalent(TOFE) sizing method is presented.

• 한국안전학회지
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• 제12권2호
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• pp.3-16
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• 1997

In modern high performance engineering applications, the structural integrity of materials and structures are quite often evaluated using fracture mechanics. This evaluation in turn requires information on the flaw geometry (location, type, shape, size, and orientation). The ultrasonic nondestructive evaluation (NDE) method is one technique that is commonly used to provide such information. Flaw classification (determination of the flaw type ) and flaw sizing (prediction of the flaw shape, orientation and sizing parameters) are very important issues for quantitative ultrasonic NDE. In this paper new approaches to both classification and sizing of flaws are described together with extensive review of previous works on both topics. In the area of flaw classification, a methodology is developed which can solve classification problems using probabilistic neural networks, and in the area of flaw sizing, a time-of-flight equivalent (TOFE) sizing method is presented. The techniques proposed here are in a form that can be used directly in many practical applications to quantitative estimates of the flaw's significance.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 1996년도 제7회 학술강연회논문집
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• pp.167-176
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• 1996

Flaw classification and sizing are very essential issues in quantitative ultrasonic nondestructive evaluation of various materials and structures including weldments. For performing of these tasks in an automated fashion, we are developing an intelligent ultrasonic evaluation system with a multi-axis portable scanner which can do consistent and efficient acquisition and processing of ultrasonic flaw signals. Here we present our efforts to develop of this intelligent system including design of the portable scanner, acquisition and processing of ultrasonic flaw signals, display of pseudo 3-D image of flaws, and classification and sizing of flaws in weldments.

• 한국생산제조학회지
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• 제9권1호
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• pp.119-127
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• 2000

In this study the classified researches the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing feature extraction feature selection and classifi-er selection is teated by bulk,. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function the empirical Bayesian classifier. Also the pattern recognition technology is applied to classifica-tion problem of natural flaw(i.e multiple classification problem-crack lack of penetration lack of fusion porosity and slag inclusion the planar and volumetric flaw classification problem), According to this result it is possible to acquire the recognition rate of 83% above even through it is different a little according to domain extracting the feature and the classifier.

• 비파괴검사학회지
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• 제19권5호
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• pp.378-391
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• 1999

• 비파괴검사학회지
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• 제21권6호
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• pp.658-662
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• 2001

후방산란된 초음파의 입사각 의존성을 이용한 표면 결함유형의 평가가 시도되었다. 평탄한 유리위에 순수한 홈, 구리로 채워진 홈, 표면위 붙여진 구리선등의 표면결함 시편에 대한 후방산란 프로파일은 제 1 임계각에서 종파의 산란과 관련된 새로운 프로파일을 보여주었다. 결함에 의한 산란효과가 클수록 후방프로파일들의 정점 위치는 작게 나타났으며 후방복사 프로파일과 정점 위치에서의 파열의 모양은 결함의 유형과 위치에 따라 누수파와 산란파의 복합적 요인에 의해 다른 형태를 보여주었다.

• 비파괴검사학회지
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• 제20권2호
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• pp.102-109
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• 2000

본 논문에서는 체적형 결함과 균열형 결함에 대한초음파산란 현상을 모델링하기 위한 두가지 이론을 설명하였다. 동탄성 Kirchhoff 근사 (EKA)와 기하학적 회절이론 (GTD)이 각각 원주형 기공과 반무한 균열에 적용되었다. 이 두 이론은 고주파수 근사법으로 알려져 있다. 모델 결함들에 평면파가 입사하는 경우의 2차원 동탄성 산란 문제를 고려하였으며 산란장을 반사계수와 회절계수의 항으로 구하였다. 원거리에서 산란파의 변위에 대한 입사파 변위의 비를 관찰 방향의 함수로 구했으며 그 결과를 경계요소법과 비교하였다.

• Nuclear Engineering and Technology
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• 제51권7호
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.304-309
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• 2003

In this study, natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.