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Automatic Classification of SMD Packages using Neural Network

신경회로망을 이용한 SMD 패키지의 자동 분류

  • Youn, SeungGeun (Dept. of Control and Robot Engineering, Chungbuk National University) ;
  • Lee, Youn Ae (Dept. of Control and Robot Engineering, Chungbuk National University) ;
  • Park, Tae Hyung (Dept. of Control and Robot Engineering, Chungbuk National University)
  • 연승근 (충북대학교 제어로봇공학과) ;
  • 이윤애 (충북대학교 제어로봇공학과) ;
  • 박태형 (충북대학교 제어로봇공학과)
  • Received : 2014.07.28
  • Accepted : 2015.01.05
  • Published : 2015.03.01

Abstract

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.

Keywords

References

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Cited by

  1. Defect Classification of Components for SMT Inspection Machines vol.21, pp.10, 2015, https://doi.org/10.5302/J.ICROS.2015.15.0019