• 반도체디스플레이기술학회지
• /
• 제15권3호
• /
• pp.18-23
• /
• 2016

QFN (Quad Flat No-leads Package) is one of the SMD (Surface Mount Device). Since there is no lead in QFN, there are many defects on solder. Therefore, we propose an efficient mechanism for QFN solder defect detection at this paper. For this, we employ Convolutional Neural Network (CNN) of the Machine Learning algorithm. QFN solder's color multi-layer images are used to train CNN. Since these images are 3-channel color images, they have a problem with applying to CNN. To solve this problem, we used each 1-channel grayscale image (Red, Green, Blue) that was separated from 3-channel color images. We were able to detect QFN solder defects by using this CNN. In this paper, it is shown that the CNN is superior to the conventional multi-layer neural networks in detecting QFN solder defects. Later, further research is needed to detect other QFN.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2004년도 추계학술대회
• /
• pp.585-590
• /
• 2004

Many machine failures are not detected well in advance due to the masking of background noise and attenuation of the source signal through the transmission mediums. Advanced signal processing techniques using adaptive filters and higher order statistics have been attempted to extract the source signal from the measured data at the machine surface. In this paper, blind deconvolution using the eigenvector algorithm (EVA) technique is used to recover a damaged bearing signal using only the measured signal at the machine surface. A damaged bearing signal corrupted by noise with varying signal-to-noise (s/n) was used to determine the effectiveness of the technique in detecting an incipient signal and the optimum choice of filter length. The results show that the technique is effective in detecting the source signal with an s/n ratio as low as 0.21, but requires a relatively large filter length.

• 한국생산제조학회지
• /
• 제22권3호
• /
• pp.452-458
• /
• 2013

Over the past several years, many studies have been carried out in the field of 3D data inspection systems. Several attempts have been made to improve the quality of manufactured parts. The introduction of laser sensors for inspection has made it possible to acquire data at a remarkably high speed. In this paper, a robust inspection technique for detecting defects in 3D pressed parts using laser-scanned data is proposed. Point cloud data are segmented for the extraction of features. These segmented features are used for shape matching during the localization process. An iterative closest point (ICP) algorithm is used for the localization of the scanned model and CAD model. To achieve a higher accuracy rate, the ICP algorithm is modified and then used for matching. To enhance the speed of the matching process, aKd-tree algorithm is used. Then, the deviation of the scanned points from the CAD model is computed.

• 제어로봇시스템학회논문지
• /
• 제2권1호
• /
• pp.26-34
• /
• 1996

One of the challenging tasks in visual inspection using CCD camera is to identify surface defects in an image with complex textured backgeound. In microscopic view, the surface of real objects shows regular or random textured patterns. In this paper, we present a visual inspection algorithm to extract abnormal surface defects in an image with textured background. The algorithm uses the space and frequency information at the same time by introducing the Dominant Frequency Map(DFM) which can describe the frequency characteristics of every small local region of an input image. We demonstrate the feasibility and effectiveness of the method through a series of real experiments for a 14" TV CRT mold. The method successfully identifies a variety of infinitesimal defects, whose size is larger than $50\mu\textrm{m}$, of the mold. The experimental results show that the DFM based method is less sensitive to the environmental changes, such as illumination and defocusing, than conventional vision techniques.ques.

• 전기학회논문지
• /
• 제56권7호
• /
• pp.1183-1190
• /
• 2007

This paper proposes the improved fault decision algorithm using DWT(Discrete Wavelet Transform) and ANNs for the FRTU(Feeder Remote Terminal Unit) on the feeder in the power distribution system. Generally, the FRTU has the fault decision scheme detecting the phase fault, the ground fault. Especially FRTU has the function for 2000ms. This function doesn't operate FI(Fault Indicator) for the Inrush current generated in switching time. But it has a defect making it impossible for the FI to be operated from the real fault current in inrush restraint time. In such a case, we can not find the fault zone from FI information. Accordingly, the improved fault recognition algorithm is needed to solve this problem. The DWT analysis gives the frequency and time-scale information. The neural network system as a fault recognition was trained to distinguish the inrush current from the fault status by a gradient descent method. In this paper, fault recognition algorithm is improved by using voltage monitoring system, DWT and neural network. All of the data were measured in actual 22.9kV power distribution system.

• 한국콘크리트학회:학술대회논문집
• /
• 한국콘크리트학회 2008년도 춘계 학술발표회 제20권1호
• /
• pp.1029-1032
• /
• 2008

최근 각 분야에서 연구 및 개발하고 있는 IT 및 자동화 경향이 건설 유지관리에도 전파됨에 따라 유지관리 로봇의 개발과 함께 개발될 비파괴 시스템의 장착이 각광 받을 것으로 예상되고 있다. 토목 구조물의 경우, 이러한 첨단기술의 발전과 더불어 유지관리 측면에서 이러한 첨단기술을 사용하는 것이 적극적으로 고려되고 있다. 그 중 NDT 비파괴 검사방법이 토목구조물의 효율적인 유지관리를 해 적합한 방법으로 사료되고 있다. 본 연구에서는 콘크리트 구조물의 내부결함 탐상을 위해서 초음파 및 레이더 장비가 적용되었으며, 이를 활용하여 실험실 및 현장실험을 통하여 그 적용성을 실험적으로 분석하였다. 실험결과, 초음파 및 레이더 장비는 콘크리트 구조물의 내부결함을 탐상하는데 효과적임으로 분석되었다. 또한 본 연구에서는 실험결과를 바탕으로, 초음파 및 레이더 장비를 적용한 콘크리트 구조물의 유지관리를 위한 알고리즘을 개발 및 제안하였다.

• 한국산업정보학회논문지
• /
• 제29권1호
• /
• pp.107-116
• /
• 2024

Copper Filter Dryer(CFD)는 냉동 및 냉방 시스템에서 냉매의 순환 시 불순물을 제거하여 깨끗한 냉매를 유지하는 역할을 하며, CFD의 결함은 냉동 및 냉방 시스템의 누수, 수명 저하 등 제품의 결함으로 이어질 수 있어 품질보증이 필수적이다. 기존에는 품질 검사 단계에서 작업자가 검사하고 결함을 판단하는 방법이 주로 사용되었으나, 이러한 방법은 주관적으로 판단하기 때문에 정확하지 못하다. 본 논문에서는 CFD 축관 및 용접 공정 과정에서 발생하는 결함을 검출하고 기존의 품질 검사를 대체하기 위해 YOLOv7 객체 감지 알고리즘을 사용하여 결함을 검출했고, F1-Score 0.954, 0.895의 검출 성능을 확인하였다. 또한, 결함 이미지의 Timestamp에 해당하는 센서 데이터 분석을 통해 용접 과정 중 발생하는 결함의 원인을 분석하였다. 본 논문은 CFD 공정 중 발생하는 결함을 검출하고 원인을 분석함으로써 제조 품질보증과 개선 방안을 제시한다.

• 반도체디스플레이기술학회지
• /
• 제11권2호
• /
• pp.45-51
• /
• 2012

Multilayer co-extrusion blown film construction is a popular technique for producing plastic films for various packaging industries. Automated detection of defective films can improve the quality of film production process. In this paper, we propose a film inspection system that can detect and classify film defects robustly. In our system, first, film images are acquired through a high speed line-scan camera under an appropriate lighting system. In order to detect and classify film defects, an inspection algorithm is developed. The algorithm divides the typical film defects into two groups: intensity-based and texture-based. Intensity-based defects are classified based on geometric features. Whereas, to classify texture-based defects, a texture analysis technique based on local binary pattern (LBP) is adopted. Experimental results revealed that our film inspection system is effective in detecting and classifying defects for the multilayer co-extrusion blown film construction line.

• 대한안전경영과학회지
• /
• 제23권3호
• /
• pp.83-88
• /
• 2021

Recently, due to the aging of workers and the weakening of the labor base in the automobile industry, research on quality inspection methods through ICT(Information and Communication Technology) convergence is being actively conducted. A lot of research has already been done on the development of an automated system for quality inspection in the manufacturing process using image processing. However, there is a limit to detecting defects occurring in the automotive sunroof sealer application process, which is the subject of this study, only by image processing using a general camera. To solve this problem, this paper proposes a system construction method that collects image information using a infrared thermal imaging camera for the sunroof sealer application process and detects possible product defects based on the SVM(Support Vector Machine) algorithm. The proposed system construction method was actually tested and applied to auto parts makers equipped with the sunroof sealer application process, and as a result, the superiority, reliability, and field applicability of the proposed method were proven.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제11권5호
• /
• pp.211-220
• /
• 2022

최근 4차 산업혁명으로 촉발된 스마트공장에 관한 연구가 활발히 진행되고 있다. 이에 따라 제조업에서는 강건한 성능의 딥러닝 기술을 바탕으로 생산성 향상과 품질 향상을 위해 다양한 연구를 진행 중이다. 본 논문은 타이어 제조공정의 육안검사 단계에서 타이어 표면 결함을 검출하는 방법에 관한 연구로서 3D 카메라를 통해 취득한 깊이 이미지를 이용한 타이어 표면 결함 검출 방법을 소개한다. 본 연구에서 다루는 타이어 표면 깊이 이미지는 타이어 표면의 얕은 깊이로 인해 발생되는 낮은 깊이 대비와 데이터 취득 환경으로 인해 기준 깊이 값의 차이가 발생하는 문제가 있다. 그리고 제조업의 특성상 검출 성능과 함께 실시간으로 처리될 수 있는 성능을 지닌 알고리즘이 요구된다. 따라서, 본 논문에서는 타이어 표면 결함 검출 알고리즘이 복잡한 알고리즘 파이프라인으로 구성되지 않도록 상대적으로 단순한 방법들을 통해 깊이 이미지를 정규화하는 방법을 연구하였으며 검출 성능과 속도를 모두 만족할 수 있는 딥러닝 방법인 YOLO V3를 이용하여 일반적인 정규화 방법과 본 논문에서 제안하는 정규화 방법의 비교 실험을 진행하였다. 실험의 결과로 본 논문에서 제안한 정규화 방법으로 mAP 0.5 기준 약 7% 성능이 향상된 것을 확인하였으며 본 논문에서 제시한 방법이 효과적임을 보였다.