• 제목/요약/키워드: Inspection Machine

검색결과 596건 처리시간 0.034초

머신비전 기반의 가전제품 표면결함 자동검출 시스템 (Automatic detection system for surface defects of home appliances based on machine vision)

  • 이현준;정희자;이장군;김남호
    • 스마트미디어저널
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    • 제11권9호
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    • pp.47-55
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    • 2022
  • 스마트팩토리 제조공정에서의 품질관리는 중요한 요소이다. 현재, 금형 공정으로 생산되는 생활가전 제조부품의 품질검사는 대부분 작업자의 육안으로 진행되고 있으며 이로 인한 검사의 오류율이 높은 실정이다. 이러한 품질공전 개선을 위하여 결함 자동검출 시스템을 설계하여 구현하였다. 제안 시스템은 특정 위치에서 고성능 스캔 카메라로 대상물을 촬영하여 영상을 획득하고, 비전검사 알고리즘에 따라 긁힘, 찍힘, 이물질에 의한 불량품을 판독한다. 본 연구에서는 긁힘에 대한 불량 인식율을 높이기 위하여 깊이 정보 기반 분기 판단 알고리즘(Depth-based branch decision algorithm, DBD)을 개발하여 정확도를 높였다.

CAPP를 위한 3차원 CAD에서의 공차정보관리에 관한 연구 (A study on 3D CAD tolerance information handling for inspection plnning)

  • 황인식;이관복;하성도
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 1995년도 추계학술대회 논문집
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    • pp.952-956
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    • 1995
  • It is known that the 3D Solid CAD system can provide various information which is useful for implementing CAPP and CAE. However the commercial 3D CAD systems available today do not support the handling of non-geometric information such as geometry tolerance and surface finish. It is impossible to input the non-geometric information during designof parts while CAPP needs the information for selecting machine tools. fiztures, inspection method, etc. In this paper the need of research on handling tolerance information In 3D CAD systems is considered. The development of inspection planning support system is also explained with an example. The development of inspection planning support systm receives the design geometry information from the 3D CAD system in the form of 2D draft and generates the inspection data base and the inspection sheet through the user interaction.

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유전 알고리즘에 의한 플랜트 보전을 위한 최적검사기간 결정 방법론 (Decision-making Method of Optimum Inspection Interval for Plant Maintenance by Genetic Algorithms)

  • 서광규;서지한
    • 산업경영시스템학회지
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    • 제26권2호
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    • pp.1-8
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    • 2003
  • The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plant maintenance by reliability theory are too complicated to be solved. Moreover, the mathematical and theoretical models are not usually cases in the practical applications. In order to overcome these problems, we propose a new the decision-making method of optimal inspection interval to minimize the maintenance cost by reliability theory and genetic algorithm (GA). The most merit of the proposed method is to decide the inspection interval for a plant machine of which failure rate $\lambda$(t) conforms to any probability distribution. Therefore, this method is more practical. The efficiency of the proposed method is verified by comparing the results obtained by GA-based method with the inspection model haying regular time interval.

3차원 X-ray 영상 기법을 이용한 전자부품 검사 기술 연구 (Study for Inspection Method of Electronic Components Using 3-D X-ray Imaging Technology)

  • 심혁훈;박기남;김종형;박희재
    • 한국공작기계학회논문집
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    • 제16권5호
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    • pp.157-161
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    • 2007
  • There are technological changes to reduce the size and weight of electronic components and to accommodate multi-functions in them. To meet this trend, more complicated technological processes are required. To maintain the processes, more accurate inspection systems are also necessary. Therefore, new inspection methods are needed, which is differ from conventional inspection methods such as electrical test methods ICT(In-Circuit Test), FCT(Function Test) and visual test using optical equipments. One of the possible approaches is non-destructive test using X-ray. In this paper, an inspection method using X-ray is developed and applied to inspection of soldering state and internal defects of electronic components.

품질 검사자의 외관검사 검출력 향상방안에 관한 연구 (A Study on the Improvement of Human Operators' Performance in Detection of External Defects in Visual Inspection)

  • 한성재;함동한
    • 대한안전경영과학회지
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    • 제21권4호
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    • pp.67-74
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    • 2019
  • Visual inspection is regarded as one of the critical activities for quality control in a manufacturing company. it is thus important to improve the performance of detecting a defective part or product. There are three probable working modes for visual inspection: fully automatic (by automatic machines), fully manual (by human operators), and semi-automatic (by collaboration between human operators and automatic machines). Most of the current studies on visual inspection have been focused on the improvement of automatic detection performance by developing a better automatic machine using computer vision technologies. However, there are still a range of situations where human operators should conduct visual inspection with/without automatic machines. In this situation, human operators'performance of visual inspection is significant to the successful quality control. However, visual inspection of components assembled into a mobile camera module belongs to those situations. This study aims to investigate human performance issues in visual inspection of the components, paying more attention to human errors. For this, Abstraction Hierarchy-based work domain modeling method was applied to examine a range of direct or indirect factors related to human errors and their relationships in the visual inspection of the components. Although this study was conducted in the context of manufacturing mobile camera modules, the proposed method would be easily generalized into other industries.

보안 점검 목록을 효율적으로 관리하기 위한 머신러닝 기반의 보안 점검 항목 분류 (Classification of Security Checklist Items based on Machine Learning to Manage Security Checklists Efficiently)

  • 박현경;안효범
    • 스마트미디어저널
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    • 제11권11호
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    • pp.75-83
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    • 2022
  • 미국의 NIST에서는 CVE나 CPE와 같은 기존의 취약점 관련 표준을 이용하여 보안 취약성 점검 및 관리를 자동화할 수 있도록 하는 프로토콜인 SCAP을 개발했다. SCAP은 XCCDF 및 OVAL 언어를 이용하여 점검파일을 작성하고 작성한 점검 파일을 OpenSCAP에서 만든 SCAP Workbench와 같은 SCAP 도구로 실행하면 점검 결과를 반환하는 식으로 동작한다. 다양한 운영체제에 대한 SCAP 점검 파일이 NCP 커뮤니티를 통해 공유되고 있으며 점검 파일에는 점검 항목별로 아이디, 제목, 설명, 점검 방법 등이 작성되어 있다. 하지만 점검항목은 단순히 작성한 순서대로 나열되어 있어 보안 관리자가 SCAP 점검 파일을 이용하여 체계적으로 관리할 수 있도록 점검 항목을 유형별로 분류하여 관리할 필요가 있다. 본 연구에서는 OVAL 언어로 작성된 SCAP 점검 파일에서 각 점검 항목에 대한 설명이 작성된 부분을 추출하여 머신러닝 모델을 통해 카테고리를 분류하고, SCAP 점검 결과를 분류한 점검 항목별로 출력하는 방법을 제안한다.