• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2012년도 추계학술대회 논문집
• /
• pp.611-612
• /
• 2012

• 동력기계공학회지
• /
• 제8권3호
• /
• pp.52-57
• /
• 2004

A new non-destructive inspection technique has been developed. One characteristic of the technique is that defects are visualized by laser ray. Magnetic domains and domain walls of a magneto-optical sensor(MO sensor) are varied by the magnetic flux leaked by defects, and the variations are observed by the reflected light of the laser ray. The information of defect can remotely be inspected by this technique in a real time. This paper describes the results estimated on the 2-dimensional surface defects and opposite-side defects in a ferromagnetic material and the natural surface defect in a clutch disk wheel. The light region of a visible image and the magnitude of a reflected light increases as the input current of the magnetizer increases. The natural surface defect, that has not the width of crack's open mouth, can be also visualized like as 2-dimensional artificial defects.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2006년도 정기 학술대회 논문집
• /
• pp.89-96
• /
• 2006

Nondestructive evaluation using surface waves needs an analytical solution for the reference value to compare with experimental data. Finite element analysis is very powerful tool to simulate the wave propagation, but has some defects. It is very expensive and high time-complexity for the required high resolution. For those reasons, it is hard to implement an optimization problem in the actual situation. The developed engine in this paper can substitute for the finite element analysis of surface waves propagation, and it accomplishes the fast analysis possible to be used in optimization. Including this artificial intelligence engine, most of soft computing algorithms can be applied on the special database. The database of surface waves propagation is easily constructed with the results of finite element analysis after reducing the dimensions of data. The principal wavelet-component analysis is an efficient method to simplify the transient wave signal into some representative peaks. At the end, artificial neural network based on the database make it possible to invent the artificial intelligence engine.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2023년도 가을학술발표대회논문집
• /
• pp.97-98
• /
• 2023

Recently, the use of digital technology in architectural technology is gradually increasing with the development of various industrial technologies. There are artificial intelligence and drones in the field of architecture, and among them, deep learning technology has been introduced to conduct research in areas such as precise inspection of buildings, and it is expressed in a highly reliable way. When a building is deteriorated, various defects such as cracks in the surface and subsidence of the structure may occur. Since these cracks can represent serious structural damage in the future, the detection of cracks was conducted using artificial intelligence that can detect and identify surface defects by detecting cracks and aging of buildings.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권12호
• /
• pp.4763-4775
• /
• 2020

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

• Restorative Dentistry and Endodontics
• /
• 제29권3호
• /
• pp.267-272
• /
• 2004

The purpose of this in vitro study was to evaluate the effect of surface defects and cross-sectional configuration of NiTi rotary files on the fatigue life under cyclic loading. Three NiTi rotary files ($K3^{TM},{\;}ProFile^{\circledR},{\;}and{\;}HERO{\;}642^{\circledR}$) with ＃30/.04 taper were evaluated. Each rotary file was divided into 2 subgroups : control (no surface defects) and experimental group (artificial surface defects), A total of six groups of each 10 were tested. The NiTi rotary files were rotated at 300rpm using the apparatus which simulated curved canal (40 degree of curvature) until they fracture. The number of cycles to fracture was calculated and the fractured surfaces were observed with a scanning electron microscope. The data were analyzed statistically. The results showed that experimental groups with surface defects had lower number of cycles to fracture than control group but there was only a statistical significance between control and experimental group in the $K3^{TM}$ (p<0.05), There was no strong correlation between the cross-sectional configuration area and fracture resistance under experimental conditions. Several of fractured files demonstrated characteristic patterns of brittle fracture consistent with the propagation of pre-existing cracks. This data indicate that surface defects of NiTi rotary files may significantly decrease fatigue life and it may be one possible factor for early fracture of NiTi rotary files in clinical practice.

• Journal of Welding and Joining
• /
• 제16권3호
• /
• pp.64-73
• /
• 1998

This paper is concerned with defects detection and evaluation of heat affected zone (HAZ) in austenitic stainless steel type 304 by ultrasonic wave and neural network. In experiment, the reflected ultrasonic defect signals from artificial defects (side hole, vertical hole, notch) of HAZ appears as beam distance of prove-defect, distance of probe-surface, depth of defect-surface on CRT. For defect classification simulation, neural network system was organized using total results of ultrasonic experiment. The organized neural network system was learned with the accuracy of 99%. Also it could be classified with the accuracy of 80% in side hole, and 100% in vertical hole, 90% in notch about ultrasonic pattern recognition. Simulation results of neural network agree fairly well with results of ultrasonic experiment. Thus were think that the constructed system (ultrasonic wave - neural network) in this work is useful for defects dection and classification such as holes and notches in HAZ of austenitic stainless steel 304.

• 한국복합재료학회:학술대회논문집
• /
• 한국복합재료학회 2001년도 춘계학술발표대회 논문집
• /
• pp.211-214
• /
• 2001

Electronic Speckle Pattern Interferometry(ESPI) is one of optical technique to measure displacement precisely, uses CCD camera to show result image in real time. General ESPI system measures in-plane or out-of-plane displacement. Shearography is one of electronic speckle pattern interferometric methods which allow full-field observation of surface displacement derivatives and it is robust in vibration. The shearography provides non-contacting technique of evaluating defects nondestructively. In this study, the shearography was used to evaluate defects in Carbon Fiber Reinforced Plastic(CFRP). Various sizes of artificial defects were embedded in various depths of woven CFRP plate. Effects due to the variation of size and depth of defects were evaluated in this study.

• 한국신뢰성학회:학술대회논문집
• /
• 한국신뢰성학회 2002년도 정기학술대회
• /
• pp.245-251
• /
• 2002

Electronic Speckle Pattern Interferometry(ESPI) is one of optical technique to measure displacement precisely, uses CCD camera to show result image in real time. General ESPI system measures in-plane or out-of-plane displacement. Shearography is one of electronic speckle pattern interferometric methods which allow full-field observation of surface displacement derivatives and it is robust in vibration. The shearography provides non-contacting technique of evaluating defects nondestructively In this study, the shearography was used to evaluate defects in Carbon Fiber Reinforced Plastic(CFRP). Various sizes of artificial defects were embedded in various depths of woven CFRP plate. Effects due to the variation of size and depth of defects were evaluated in this study.

• 비파괴검사학회지
• /
• 제18권6호
• /
• pp.445-454
• /
• 1998

본 연구는 박판 용접부 결함 검출 기법의 확립을 위해서 실시된 기초 연구로서, 전기강판 소재의 모재에 인위적인 결함을 작성하고, 이론 및 시험적 결과를 이용하여 결함을 검출하기 위한 최적의 조건과 임계 검출 결함 크기를 조사한 것이다. 이를 위해서 소재의 dispersion curve를 구하고, 두께 2.4mm의 박판에 대해서 tone burst방식에 의한 초음파 탐상을 실시하였다. 실험적 검토를 행한 결과 840kHz의 가진 주파수와 30도 그리고 85도의 입사각이 최적의 탐상 조건임을 알았다. 한편, 초음파의 속도와 dispersion커브를 비교 검토하여 본 바, 30도의 입사각에서 발생하여 전파하는 초음파는 symmetic mode이고 85도의 입사각에서는 antisymmetric mode의 파가 전파하고 있었다. 결함의 위치와 형상에 따라 반사파의 특성이 다르게 나타나고 있었으며, 특히 표면 결함의 경우에는 antisymmetric 모드의 초음파가 symmetric 모드 보다 높은 반사파 에너지를 나타내고 있었다. 또한 이러한 초음파 모드의 종류와 결함 검출과의 관계에 대해서는 유도파의 구조에 의해서 설명이 가능했다.