• 한국안전학회지
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• 제34권4호
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• pp.15-21
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• 2019

In order to secure the safety of various machinery, it is very important to develop a technique for accurately and quickly measuring the cracks generated in the mechanical equipment and evaluating the mechanical characteristics. The evaluation of the mechanical properties is accompanied by an appropriate strain measurement according to the material and crack occurrence of the target structure. Especially, when micro cracks are generated, the evaluation method is very important. Digital image correlation is an optical full field displacement measuring method which is using currently with speckles in the interested area. However the evaluation method and conditions of image distributions have to be considered carefully to measure the crack occurrence because the images of the speckle patterns affect the quality of displacement results. In this study, the speckle pattern density is characterized to improve the accuracy of the measurement method. And also the micro crack initiation is detected by the measured displacement in the adopted speckle pattern distribution. It is shown that the proposed method is useful to determine the density pattern distribution for the accurate measurement and crack detection.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2006년도 제26회 춘계학술대회논문집
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• pp.239-245
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• 2006

초음파를 이용한 추진기관의 비파괴검사는 X-ray 검사에 비하여 경제성이 우수하고, X-ray 검사시 취약한 dis-bond, damage 등의 결함 검출이 우수한 편이다. 그리고 전용시설의 필요없이 현장에서 실시간으로 검사가 가능하며 방사선 작업에 비하여 안전한 방법이다. 본 논문에서는 고체 추진제에 대한 초음파 특성을 분석하고, 추진제/라이너 미접착에 대한 내측과 외측 검사 방법 및 추진제 damage에 의한 미세 크랙검출에 대하여 실험 및 분석하였다. 실험 결과, 추진제에서의 초음파 감쇠는 $6\sim8db/cm$로 비교적 큰 감쇠를 보였으며 추진제/라이너 미접착에 대한 내 외측 검사에 있어서도 제한된 조건이지만 검출 가능성을 보였다. 그리고 damage에 의한 추진제 미세 크랙도 초음파의 감쇠특성을 이용하여 검출 가능함을 보였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2003년도 학술.기술논문 발표회
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• pp.27-31
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• 2003

To maintain serviceability of concrete structure more than proper it is necessary not only predict service life through periodical monitor but also need monitoring system to recognize optimal time and method for repair. Recently, CPGFRP, replacing some GFRP with CF, is developed and used for monitoring concrete fraction. But dramatic resistance change of CPGFRP is showed below 0.5% strain and it is not small strain in terms of monitoring micro crack in concrete. In other word, monitoring with CF is not suitable in low stress hut hight stress. In this study, we accessed applicable possibility and reliability of CPGFRP composite as monitoring sense that is proved very sensitive to stress through domestic and oversea previous study. CPGFRP composite plays a role in specimen like steel and increases flexural strength. CPGFRP composite shows resistance increasement in micro crack. In particular, CPUFRP is more sensitive than strangage in low stress. Resistance change ratio curve is very similar to strain curve so sensitivity and reliability is very excellent to monitor concrete fracture.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2003년도 학술.기술논문발표회
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• pp.27-31
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• 2003

To maintain serviceability of concrete structure more than proper it is necessary not only predict service life through periodical monitor but also need monitoring system to recognize optimal time and method for repair. Recently, CPGFRP, replacing some GFRP with CF, is developed and used for monitoring concrete fraction. But dramatic resistance change of CPGFRP is showed below 0.5％ strain and it is not small strain in terms of monitoring micro crack in concrete. In other word, monitoring with CF is not suitable in low stress but hight stress. In this study, we accessed applicable possibility and reliability of CPGFRP composite as monitoring sense that is proved very sensitive to stress through domestic and oversea previous study. CPGFRP composite plays a role in specimen like steel and increases flexural strength. CPGFRP composite shows resistance increasement in micro crack. In particular, CPGFRP is more sensitive than strangage in low stress. Resistance change ratio curve is very similar to strain curve so sensitivity and reliability is very excellent to monitor concrete fracture.

• Computers and Concrete
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• 제32권5호
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• 대한기계학회논문집A
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• 제24권4호
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• pp.1001-1006
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• 2000

To verify the existing theory, non-propagating crack(NPC) does not exist in Ti which fulfills the good conditions for being of NPC, NPC detection in Ti was tried out. Also, the conception of fatigue limit in Ti and a main cause for NPC being were inquired. NPC was detected in both sharp notch root ( $\rho$=0.02mm) and micro pit (diameter = 0.25mm) which held fast to the end under stressing of fatigue limit. Therefore, the existing theory was identified as mistake. But, NPC can not be detected in smooth specimen. This fact would be due to the presumption that NPC is very small or crack does not initiate in smooth specimen. Anyway, the fatigue limit of Ti does not correspond to critical stress of crack initiation but correspond to critical stress of NPC growth. Measurement on the COD of NPC in Ti showed that the crack tip was closed even under the peak stress level at fatigue limit. But, after stress relieving annealing crack tip was opened. Consequently, compressive residual stress which is induced around the crack tip is considered to be the factor causing the NPC being.

• Journal of Welding and Joining
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• 제30권6호
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• pp.80-85
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• 2012

Fatigue cracks in structural components are the most common cause of structural failure when exposed to fatigue loading. In this respect, fatigue crack detection and structural health assessment are very important. Currently, various smart materials are used for detecting fatigue crack and measurement of SIFs(Stress Intensity Factors). So, this paper presented a measurement of SIFs using MFC(Micro Fiber Composite) sensor which is the one of the smart material. MFC sensor is more flexible, durable and reliable than other smart materials. The SIFs of Mode I(K I) as well as Mode II(K II) based on the piezoelectric constitutive law and fracture mechanics are calculated. In this study, the SIF values measured by MFC sensors are compared with the theoretical results.

• 한국정밀공학회지
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• 제32권5호
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• pp.463-470
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• 2015

Solar cell is typical representative of renewable green energy. Silicon wafer contributes about 66 percent to its cost structure. In its manufacturing, micro-cracks are often occurred due to manufacturing process such as wire sawing, grinding and cleaning. Their detection and classification are important to process feedback information. In this paper, a classification method of micro-cracks is proposed, based on the fusion of principal component analysis(PCA) and neural network. The proposed method shows that it gives higher results than single application of two methods, in terms of shape and size classification of micro-cracks.

• 반도체디스플레이기술학회지
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• 제12권2호
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• pp.57-61
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• 2013

This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

• 터널과지하공간
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• 제29권3호
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• pp.184-196
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• 2019

본 연구에서는 화강암 시편에서 수압 파쇄법에 의해 생성된 미세균열의 3차원 형상을 X-ray CT 영상과 딥러닝을 이용하여 추출하였다. 실험으로 생성된 미세균열은 X-ray CT 영상 상에서 일반적인 영상처리방법으로는 추출하기 매우 어렵고 육안으로만 관찰이 가능한 형태를 지닌다. 하지만 본 연구에서 제안한 합성곱 신경망(Convolutional neural network) 기반 인코더-디코더(Encoder-Decoder) 구조의 딥러닝 모델을 통해 미세균열을 정량적으로 추출할 수 있었다. 특히 픽셀 단위의 미세균열 추출을 위해 인코딩 과정에서 소실되는 정보를 디코딩 과정으로 직접 전달하는 디코더 모델을 제안하였다. 또한, 딥러닝 기반 신경망 학습에 필요한 데이터의 수를 증가시키기 위해 이미지의 분할(Division), 회전(Rotation), 그리고 반전(Flipping) 등으로 데이터를 생성하는 영상 증대 방법을 적용하였으며 이때 최적의 조합을 확인하였다. 최적의 영상 학습 데이터 증대 방법을 적용하였을 때 검증 데이터뿐만 아니라 테스트 데이터에서의 성능 향상을 확인하였다. 학습 데이터의 원본 개수가 딥러닝 기반 신경망의 균열 추출 성능에 미치는 영향을 확인하고 딥러닝 기술을 사용하여 성공적으로 미세균열을 추출하였다.