• 제목/요약/키워드: Defects Detection

검색결과 758건 처리시간 0.028초

AFVI를 위한 PCB PAD의 자동 광학 검사 (Automatic Optical Inspection of PCB PADs for AFVI)

  • 문순환
    • 한국광학회:학술대회논문집
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    • 한국광학회 2006년도 하계학술발표회 논문집
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    • pp.469-471
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    • 2006
  • This paper describes a efficient insepction method of PCB PADs for AFVI. The methods for PCB inspection have been tried to detect the defects in PCB PADs, but their low detection rate results from pattern variations that are originating from etching, printing and handling processes. The adaptive inspection method has been newly proposed to extract minute defects based on dynamic segments and filters. The vertexes are extracted from CAM master images of PCB and then a lot of segments are constructed in master data. The proposed method moves these segments to optimal directions of a PAD contour and so adaptively matches segments to PAD contours of inspected images, irrespectively of various pattern variations. It makes a fast, accurate and reliable inspection of PCB patterns. Experimental results show that proposed methods are found to be effective for flexible defects detection.

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Flaw Detection in Ceramics using Hough transform and Least squares

  • Hong, Dong-Jin;Cha, Eui-Young
    • 한국컴퓨터정보학회논문지
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    • 제20권10호
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    • pp.23-29
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    • 2015
  • In this paper, we suggest a method of detecting defects by applying Hough transform and least squares on ceramic images obtained from non-destructive testing. In the ceramic images obtained from non-destructive testing, the background area, where the defect does not exist, commonly show gradual change of luminosity in vertical direction. In order to extract the background area which is going to be used in the detection of defects, Hough transform is performed to rotate the ceramic image in a way that the direction of overall luminosity change lies in the vertical direction as much as possible. Least squares are then applied on the rotated image to approximate the contrast value of the background area. The extracted background area is used for extracting defects from the ceramic images. In this paper we applied this method on ceramic images acquired from non-destructive testing. It was confirmed that extracted background area could be effectively applied for searching the section where the defect exists and detecting the defect.

열전달 시뮬레이션을 통한 최적공극탐지 차트개발 (Development of an Optimum Void Detection Chart using Heat Transfer Simulation)

  • 최현호;박진형;지광습
    • 한국콘크리트학회:학술대회논문집
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    • 한국콘크리트학회 2006년도 추계 학술발표회 논문집
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    • pp.241-244
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    • 2006
  • It is essential to develop a large capacity, non-contact nondestructive inspection system having high reliability to investigate repaired and strengthened structures. Nowadays, an infrared camera is widely used in non-contact nondestructive inspection system. Because an infrared camera is sensitive to the surrounding environment, it is necessary to improve a sensitivity of thermal image information and a relationship between defects and thermal image information. In this papaer, presented is an optimum void detection chart for the optimum conditions to detect infrared rays from inside and outside defects like voids and cracks in concrete structures using extensive computer simulation. Sensitivity studies are performed with respect to variables influencing the temperature distribution such as heating temperature, heating time, and geometries of defect, etc. It may be stated that it could be successfully utilized for the non-contact nondestructive inspection system to detect defects in concrete structures.

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Detection of Subsurface Defects in Metal Materials Using Infrared Thermography; Image Processing and Finite Element Modeling

  • Ranjit, Shrestha;Kim, Won Tae
    • 비파괴검사학회지
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    • 제34권2호
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    • pp.128-134
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    • 2014
  • Infrared thermography is an emerging approach to non-contact, non-intrusive, and non-destructive inspection of various solid materials such as metals, composites, and semiconductors for industrial and research interests. In this study, data processing was applied to infrared thermography measurements to detect defects in metals that were widely used in industrial fields. When analyzing experimental data from infrared thermographic testing, raw images were often not appropriate. Thus, various data analysis methods were used at the pre-processing and processing levels in data processing programs for quantitative analysis of defect detection and characterization; these increased the infrared non-destructive testing capabilities since subtle defects signature became apparent. A 3D finite element simulation was performed to verify and analyze the data obtained from both the experiment and the image processing techniques.

방사선 투과 이미지에서의 용접 결함 검출을 위한 딥러닝 알고리즘 비교 연구 (Comparative Study of Deep Learning Algorithm for Detection of Welding Defects in Radiographic Images)

  • 오상진;윤광호;임채옥;신성철
    • 한국산업융합학회 논문집
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    • 제25권4_2호
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    • pp.687-697
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    • 2022
  • An automated system is needed for the effectiveness of non-destructive testing. In order to utilize the radiographic testing data accumulated in the film, the types of welding defects were classified into 9 and the shape of defects were analyzed. Data was preprocessed to use deep learning with high performance in image classification, and a combination of one-stage/two-stage method and convolutional neural networks/Transformer backbone was compared to confirm a model suitable for welding defect detection. The combination of two-stage, which can learn step-by-step, and deep-layered CNN backbone, showed the best performance with mean average precision 0.868.

Deep Learning-Based Defect Detection in Cu-Cu Bonding Processes

  • DaBin Na;JiMin Gu;JiMin Park;YunSeok Song;JiHun Moon;Sangyul Ha;SangJeen Hong
    • 반도체디스플레이기술학회지
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    • 제23권2호
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    • pp.135-142
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    • 2024
  • Cu-Cu bonding, one of the key technologies in advanced packaging, enhances semiconductor chip performance, miniaturization, and energy efficiency by facilitating rapid data transfer and low power consumption. However, the quality of the interface bonding can significantly impact overall bond quality, necessitating strategies to quickly detect and classify in-process defects. This study presents a methodology for detecting defects in wafer junction areas from Scanning Acoustic Microscopy images using a ResNet-50 based deep learning model. Additionally, the use of the defect map is proposed to rapidly inspect and categorize defects occurring during the Cu-Cu bonding process, thereby improving yield and productivity in semiconductor manufacturing.

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유도초음파를 이용한 박판에서의 결함의 검출에 관한 연구 (Detection of Defects in a Thin Steel Plate Using Ultrasonic Guided Wave)

  • 정희돈;신현재
    • 비파괴검사학회지
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    • 제18권6호
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    • pp.445-454
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    • 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.

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자기광학센서를 이용한 강자성체 결함 탐상 (The Detection of Defects in Ferromagnetic Materials Using Magneto-Optical Sensor)

  • 김훈
    • 동력기계공학회지
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    • 제8권3호
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    • pp.52-57
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    • 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.

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Implementation of a Modified SQI for the Preprocessing of Magnetic Flux Leakage Signal

  • Oh, Bok-Jin;Choi, Doo-Hyun
    • Journal of Magnetics
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    • 제18권3호
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    • pp.357-360
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    • 2013
  • A modified SQI method using magnetic leakage flux (MFL) signal for underground gas pipelines' defect detection and characterization is presented in this paper. Raw signals gathered using MFL signals include many unexpected noises and high frequency signals, uneven background signals, signals caused by real defects, etc. The MFL signals of defect free pipelines primarily consist of two kinds of signals, uneven low frequency signals and uncertain high frequency noises. Leakage flux signals caused by defects are added to the case of pipelines having defects. Even though the SQI (Self Quotient Image) is a useful tool to gradually remove the varying backgrounds as well as to characterize the defects, it uses the division and floating point operations. A modified SQI having low computational complexity without time-consuming division operations is presented in this paper. By using defects carved in real pipelines in the pipeline simulation facility (PSF) and real MFL data, the performance of the proposed method is compared with that of the original SQI.