Automated scrap-sorting research using a line-scan camera system

라인스캔 카메라 시스템을 이용(利用)한 스크랩 자동선별(自動選別) 연구(硏究)

  • Published : 2008.12.27

Abstract

In this study, a scrap sorting system using a color recognition method has been developed to automatically sort out specified materials from a mixture, and its application as been examined in the separation of Cu and other non-ferrous metal parts from a mixture of iron scraps. The system is composed of three parts; measuring, conveying and ejecting parts. The color of scrap surface is recognized by the measuring part consisting of a line-scan camera, light sources and a frame grabber. The recognition is program-controlled by a image processing algorithms, and thus only the scrap part of designated color is separated by the use of air nozzles. In addition, the light system is designed to meet a high speed of sorting process with a frequency-variable inverter and the air nozzled ejectors are to be operated by an I/O interface communication with a hardware controller. In the functional tests of the system, its efficiency in the recognition of Cu scraps from its mixture with Fe ones reaches to more than 90%, and that in the separation more than 80% at a conveying speed of 25 m/min. Therefore, it is expected that the system can be commercialized in the industry of shredder makers if a high efficiency ejecting system is realized.

본 연구에서는 라인스캔 카메라를 이용한 색도인식 스크랩 선별시스템을 설계 제작하고 제작한 시스템을 이용하여 철스크랩에 혼합되어 있는 Cu 스크랩을 자동으로 분리하는 연구를 수행하였다. 스크랩 자동선별 시스템은 크게 측정부, 이송부 그리고 이젝터로 구분되며 라인스캔 카메라, 광원 및 frame grabber로 구성된 측정부에서 스크랩 표면의 색도를 이메지 프로쎄싱 알고리즘에 의해 인식함으로써. 임의로 지정한 특정한 표면색상의 스크랩만에 에어노즐을 작동케 하여 선별하도록 되어 있다. 본 연구에서는 선별처리의 고속화에 대응하기 위하여 주파수 가변 광원시스템을 제작하여 선별시스템에 적용하였으며, 최적실험조건으로 스크랩 이송속도 25 m/min.에서 철스크랩중에 포함되어 있는 Cu스크랩을 90%이상 인식하여 약 80%의 선별효율을 얻었다.

Keywords

References

  1. C. Boukouvalas, F. D. Natale, G. D. Toni, et al., 1998 : Automatic system for surface inspection and sorting of tiles, J. Mater. Proc. Tech., 82, pp. 179-188 https://doi.org/10.1016/S0924-0136(98)00044-2
  2. D. Wang, J. Zou, and Y. Yang, 1996 : Agricultural produce grading and sorting system using color CCD and color identification algorithm, Proceedings of SPIE, 2899, pp. 637-645
  3. D. Lee and R. S. Anbalagan, 1995 : High-speed automated color sorting vision system, Proceedings of SPIE, 2622, pp. 573-579
  4. F. Pla, J. M. Sanchiz, and J. S. Sanchez, 2001 : An integral automation of industrial fruit and vegetable sorting by machine vision, IEEE Symposium on Emerging Technologies and Factory Automation, ETFA, 2, pp. 541-546
  5. J. M. Oestreich, W. K. Tolley, and D. A. Rice, 1995 : The development of color sensor system to measure mineral composition, Mineral Eng., 8(1/2), pp. 31-39 https://doi.org/10.1016/0892-6875(94)00100-Q
  6. A. M. Sabatani, V. Genovese, E. Guglielmelli, et al. : A Low-cost, composite sensor array combining ultrasonic and infrared proximity sensors, IEEE International Conference on Intelligent Robots and Systems, 3, pp. 120-126
  7. J. E. Gebhardt, W. K. Tolley, and J. H. Ahn, 1993 : Color measurements of minerals and mineralized froths, Miner. Metall. Process., May, pp. 96-99
  8. 김찬욱, 김행구, 2006 : 머신비젼 시스템을 이용한 스크랩 자동선별연구, 자원리싸이클링, 15(6), pp. 3-9