• 제목/요약/키워드: AOI (Automatic Inspection Machine)

검색결과 2건 처리시간 0.014초

검사지연시간을 고려한 SMT 검사기의 통합적 경로 계획 알고리즘 (Unified Approach to Path Planning Algorithm for SMT Inspection Machines Considering Inspection Delay Time)

  • 이철희;박태형
    • 제어로봇시스템학회논문지
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    • 제21권8호
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    • pp.788-793
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    • 2015
  • This paper proposes a path planning algorithm to reduce the inspection time of AOI (Automatic Optical Inspection) machines for SMT (Surface Mount Technology) in-line system. Since the field-of-view of the camera attached at the machine is much less than the entire inspection region of board, the inspection region should be clustered to many groups. The image acquisition time depends on the number of groups, and camera moving time depends on the sequence of visiting the groups. The acquired image is processed while the camera moves to the next position, but it may be delayed if the group includes many components to be inspected. The inspection delay has influence on the overall job time of the machine. In this paper, we newly considers the inspection delay time for path planning of the inspection machine. The unified approach using genetic algorithm is applied to generates the groups and visiting sequence simultaneously. The chromosome, crossover operator, and mutation operator is proposed to develop the genetic algorithm. The experimental results are presented to verify the usefulness of the proposed method.

신경회로망을 이용한 SMD 패키지의 자동 분류 (Automatic Classification of SMD Packages using Neural Network)

  • 연승근;이윤애;박태형
    • 제어로봇시스템학회논문지
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    • 제21권3호
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    • pp.276-282
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    • 2015
  • This paper proposes a SMD (surface mounting device) classification method for the PCB assembly inspection machines. The package types of SMD components should be classified to create the job program of the inspection machine. In order to reduce the creation time of job program, we developed the automatic classification algorithm for the SMD packages. We identified the chip-type packages by color and edge distribution of the images. The input images are transformed into the HSI color model, and the binarized histroms are extracted for H and S spaces. Also the edges are extracted from the binarized image, and quantized histograms are obtained for horizontal and vertical direction. The neural network is then applied to classify the package types from the histogram inputs. The experimental results are presented to verify the usefulness of the proposed method.