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서호건,Jin-Gyum Kim,윤성희,장경영 한국정밀공학회 2015 International Journal of Precision Engineering and Vol.16 No.13
The laser ultrasonic technique in the ablation regime was studied for the effective excitation of ultrasound. First, the optimal laser beam intensity to maximize the amplitude of ultrasound was obtained. This is useful because the amplitude of ultrasound does not always increase as the laser beam intensity increases due to the plasma shielding effect. When the laser energy is fixed, for a steel specimen, the maximum ultrasonic amplitude is obtained at the laser beam intensity in the range of 10-20 GW/cm2. In addition, an inline method to obtain the optimal laser beam intensity was proposed based on the phenomena that the amplitude of the air-borne sound induced by plasma is inversely proportional to the amplitude of the generated ultrasound. Experimental results verified the usefulness of the proposed method by showing that the amplitude of ultrasound reached its maximum when the plasma-induced airborne sound disappeared.
서호건,김명환,최성호,김정석,장경영 한국비파괴검사학회 2012 한국비파괴검사학회지 Vol.32 No.4
Using a single-line pulsed laser beam is well known as a useful noncontact method to generate a directional surface acoustic wave. In this method, different laser beam energy profiles produce different waveforms and frequency characteristics. In this paper, we considered two typical kinds of laser beam energy profiles, Gaussian and square-like, to find out a difference in the frequency characteristics. To achieve this, mathematical models were proposed first for Gaussian laser beam profile and square-like respectively, both of which depended on the laser beam width. To verify the theoretical models, experimental setups with a cylindrical lens and a line-slit mask were respectively designed to produce a line laser beam with Gaussian spatial energy profile and square-like. The frequency responses of the theoretical models showed good agreement with experimental results in terms of the existence of harmonic frequency components and the shift of the first peak frequencies to low.
Improvement of Crack Sizing Performance by using Nonlinear Ultrasonic Technique
서호건,장경영,Kyung-Cho Kim,홍동표 한국정밀공학회 2014 International Journal of Precision Engineering and Vol.15 No.11
The nonlinear ultrasonic technique (NUT) based on the contact acoustic nonlinearity (CAN) has been considered as a promisingmethod for the closed crack detection. However, most of the previous studies were limited to the modeling of the second-orderharmonic wave generation at contacted interfaces and its verification by testing artificially contacted interfaces in the throughtransmissionmethod. In this study, we investigated experimentally the contact acoustic nonlinearity at a real crack by using themeasurement system constructed in the pitch-catch method that permits the transducers to access the only single side of a teststructure. Results showed that the magnitude of the second-order harmonic wave represented the existence of the closed area clearlyand that the crack sizing performance was greatly improved by the combination of the linear and nonlinear ultrasonic techniques.
서호건,송동기,장경영 한국비파괴검사학회 2016 한국비파괴검사학회지 Vol.36 No.2
Measurement of elastic constants is crucial for engineering aspects of predicting the behavior of materials under load as well as structural health monitoring of material degradation. Ultrasonic velocity measurement for material properties has been broadly used as a nondestructive evaluation method for material characterization. In particular, pulse-echo method has been extensively utilized as it is not only simple but also effective when only one side of the inspected objects is accessible. However, the conventional technique in this approach measures longitudinal and shear waves individually to obtain their velocities. This produces a set of two data for each measurement. This paper proposes a simultaneous sensing system of longitudinal waves and shear waves for elastic constant measurement. The proposed system senses both these waves simultaneously as a single overlapped signal, which is then analyzed to calculate both the ultrasonic velocities for obtaining elastic constants. Therefore, this system requires just half the number of data to obtain elastic constants compared to the conventional individual measurement. The results of the proposed simultaneous measurement had smaller standard deviations than those in the individual measurement. These results validate that the proposed approach improves the efficiency and reliability of ultrasonic elastic constant measurement by reducing the complexity of the measurement system, its operating procedures, and the number of data
펄스 와전류 시계열 데이터 딥러닝을 통한 배관 두께 추정
서호건(Hogeon Seo),전지현(Jihyun Jun),신정우(Jeong Woo Shin),박덕근(Duck-Gun Park) 한국비파괴검사학회 2021 한국비파괴검사학회지 Vol.41 No.3
와전류 탐상법은 전도성이 있는 검사체에 대한 비파괴평가에 효과적이지만, 신호를 판독함에 있어 검사자의 높은 숙련도를 요구한다. 본 연구는 펄스 와전류 탐상으로 획득한 시계열 데이터 기반 딥러닝을 통해 배관 두께를 추정함으로써 검사자의 의존성이 보완될 수 있음을 확인하였다. 이를 위해, 9단계의 배관 두께에 대해 각 배관 두께별로 8개 동경 지점에서 10회씩 측정하여 총 720개 데이터를 수집하였다. 이를 9:1 비율로 분리해 각각 학습과 평가에 사용하였다. 심층신경망은 2차원 이미지 데이터 분류에 활용되는 인셉션 모델을 1차원 시계열 데이터를 입력 받아 연산하도록 구성했다. 평균 절대 오차를 평가지표로 삼았고, 샘플링 길이와 이동 평균 적용 여부, 학습 시의 배치 크기에 따른 평균 절대 오차를 비교했다. 이로부터 시계열 데이터 기반 딥러닝을 통해 펄스 와전류 신호로부터 배관 두께를 추정할 수 있음을 확인했다. Eddy current testing is effective in the nondestructive evaluation of conductive specimens; however, high proficiency of an inspector is required in signal interpretation. This study confirmed that the dependence of the inspector can be complemented by estimating the pipe thickness via deep learning based on time series data acquired by pulse eddy current detection. In this study, a total of 720 data were collected by measuring ten times at eight longitude points for each of nine pipe thicknesses. They were separated by a 9:1 ratio and used for learning and evaluation, respectively. A deep neural network was built by modifying the Inception model used for classifying two-dimensional image data to input and operate one-dimensional time series data. Mean absolute error (MAE) was used as an evaluation index, and MAE values were compared according to the sampling length, moving averaging, and batch size in deep learning. Consequently, it was confirmed that the pipe thickness could be estimated from the pulsed eddy current signal by deep learning based on time series data.
영상 기반 딥러닝을 통한 배관 진동 주파수 가시화 기술
서호건(Hogeon Seo),김선진(Seon-Jin Kim),정변영(Byun-Young Chung),최영철(Young-Chul Choi) 한국소음진동공학회 2022 한국소음진동공학회 논문집 Vol.32 No.1
Pipe systems in industries function similar to blood vessels in the human body. Pipe vibration is a natural phenomenon caused by external motors and fluid flow in the pipe. However, any unfavorable factors, such as in-wall collision by loose parts or unusual fluid flow, can significantly affect the vibration, which results in abnormal vibration patterns when compared to those during regular operation. For this reason, pipe vibration frequency is one of the important parameters to monitor in structural health monitoring. Therefore, a monitoring system that measures the vibration frequency of each pipe area helps to detect these anomalies early. In this study, a multi-kernel neural network was applied to visualize the vibration frequency of pipe areas using a multi-kernel neural network, by analyzing the characteristics of pixel-wise color variations in video data. The results showed that the vibration areas can be visualized using the color that corresponds to the frequency. The proposed model can be utilized for anomaly detection based on pipe vibration monitoring.
서호건(Hogeon Seo),정변영(Byeonyeong Jeong),전지현(Jihyun Jun),최영철(Young-Chul Choi) 한국소음진동공학회 2021 한국소음진동공학회 논문집 Vol.31 No.5
Piping is a part of industrial structures that acts like human blood vessels. Since pipe leakages are a threat to the integrity of a structure, it is one of the major monitoring targets. If inspectors are unaware of leakages, access to pipes for inspection can cause serious injury to the human body. Therefore, it is necessary to operate a monitoring system that detects pipe leakage regions for the safety of not only facilities but also inspectors. In this study, a multi-kernel neural network was introduced to visualize the pipe leakage regions through deep learning of the characteristics of pixel- wise color variation in normal and leakage regions from camera footage. Furthermore, we present the results of properly adjusting the visualization properties through an analysis of precision and recall according to the threshold for leakage judgment based on the output of deep learning. The results show that leakage areas can be visualized in accordance with the leakage diagnosis environment and purpose by adjusting the threshold.