DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Jung-Ryul | - |
dc.contributor.advisor | 이정률 | - |
dc.contributor.author | Majeed, Hira | - |
dc.date.accessioned | 2022-04-27T19:32:46Z | - |
dc.date.available | 2022-04-27T19:32:46Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963335&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/296261 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 항공우주공학과, 2021.8,[vi, 58 p. :] | - |
dc.description.abstract | Nondestructive testing frequently requires laser generation and sensing. This is done in order to inspect the material properties. However, thick specimens pose a major obstacle for testing with ultrasound methods, like, pulse-echo ultrasonic propagation imaging systems that need the ultrasound wave to travel through twice the total thickness of the specimen. Hence, through-transmission ultrasonic testing is required. Alignment of laser ultrasonic beams in through-transmission mode, where both beams are hitting the surface of the specimen from the opposite sides, is not only crucial yet cumbersome for better visualization of the defects. In this thesis, we automatically align the generation and sensing laser beams for through-transmission ultrasonic propagation imaging systems, by studying the effects of laser pulse energy, time of arrival and amplitude of the signal on the laser-induced through-transmission ultrasound wave and their interrelationships with the material properties. Alignment of the beams would greatly improve the propagation depth and optimize the inspection in full-field through-transmission ultrasonic propagation imaging system for thick composites and pressure vessels. In this system, we have also developed an automatic defect visualization algorithm based on machine learning. After C-Scan inspection, the time series data was processed to extract features and converted into time-frequency images using STFT and power spectrogram. It was classified into artificial defect, natural defect and fiber by a machine-learning algorithm called LSTM network. The training set showed an accuracy of 93.2% and testing set reached the accuracy of 88.2%. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Parametric study of laser ultrasound▼aAlignment of beams▼aThrough-transmission mode▼aCoincidence laser beams▼aPressure vessels▼aConvolutional neural network▼aTime series data | - |
dc.subject | 레이저 초음파▼a레이저 빔의 정렬▼a투과 모드▼a레이저 빔의 일치▼a압력 용기▼a합성곱 신경망▼a시간 영역 데이터 | - |
dc.title | Automation of alignment of sensing & generation laser beams and defect classification for through-transmission ultrasonic imagers | - |
dc.title.alternative | 투과식 초음파전파영상화 시스템의 초음파 생성 및 센싱 빔의 정렬과 결함분류 자동화 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :항공우주공학과, | - |
dc.contributor.alternativeauthor | Hira Majeed | - |
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