Detecting a defective pin through the image processing in the production line

Document Type : Manufacturing and Production

Authors

1 Faculty of engineering

2 Imam Hossein Comprehensive University

3 mechanical engineering, Eyvanekey University

Abstract

Defective pieces detection is one of the processes which is done by workers during production. In this article, a plan was offered for increasing the speed of dimensional controlling of the pin type pieces, before consuming in production line. For this purpose, pin dimensions were determined in the laboratory by computer and the coordinate measuring machine. Also, the dimensions of input pins were recorded by cameras, before using in production. These two groups of data were compared by Matlab software. In the next step, the desired tolerance dimensions were given to the software and the pins with mismatch dimensions were specified. It should be noted that the difference in the output from the software and the measurements by the coordinate measuring device was 0.2 mm. This difference could be decreased by increasing the resolution of the camera. Due to the position of the camera at the top of the work screen, the position of the pins is not limited in angle and shape. In this paper, the time period of control of pin dimensions has significantly decreased in comparison to manual control by manpower. It is also possible to examine the dimensions of several pieces simultaneously.

Keywords


  1. Sharifzadeh, M., Alirezaee, S., Amirfattahi, R. and Sadri, S. “Detection of Steel Defect Using the Image Processing Algorithms”; Int. Multitopic Conf. Karachi, Pakistan, November 1, 2008.##
  2. Golestan, A. “Easy editing and image processing”; Arad Book, Kahkeshane Danesh, pp. 56, 2008. (In Persian).##
  3. Pearosn, T. “Hardware-based image processing for high-speed inspection of grains”, Comput. Electron. Agric. Vol. 69, No. 1, pp. 12-18, 2009.##
  4. Audem, K., Orhan, U., Hekim, M. “Image processing based quality control of the impermeable seams in multilayered aseptic packages”, Expert Syst. Appl. Vol. 42, No. 7, pp. 3785-3789, 2015.##
  5. Gheorghia, C. “Industrial Image Processing Using Fuzzy-Logic”, Procedia Eng. Vol. 100, No. 1, pp. 492–498, 2015.##
  6. Zhang, X., Ding, Y., Shi, A., Liang, R. “A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM”, Expert Syst. Appl. Vol. 38, No. 5, pp. 5930–5939, 2011.##
  7. Cord, A., Bach, F., Jeulin, D. “Texture classification by statistical learning from morphologicalimage processing. pplication to metallic surfaces”, J. Microsc. Vol. 239,  No. 1, pp. 159–166, 2010.##
  8. Zheng, H., Kong, L., Nahavandi, S. “Automatic inspection of metallic surface defects using genetic algorithms”, J. Mater. Process. Technol. Vol. 125, No.1, pp. 427-433, 2002.##
  9. Pernkopf, F., Oleary, P. “Visual Inspection of Machined MetallicHigh- Precision Surfaces”, EURASIP J. Adv. Signal Process. Vol. 18, No. 79, pp. 667–678, 2002.##
  10. Smith, M., Stamp, R. “Automated inspection of textured ceramic tiles”, Comput. Ind. Vol. 43, No.1, pp. 73–82, 2000.##
  11. Ghezavati, J., Abasgholipour, M., Lotfi, A. “Detecting metallic surface defects on harvest machins using machin vision”; eighth national eng. Cong. harvest mach. 2013. (In Persian)##
  12. Khodaei, S., Allahverdizadeh, A., Dadashzadeh, B. “Design and fabrication of an autonomous mobile robot equipped with color lasers and its trajectory control based on machine vision”, J Modares Mech Eng, Vol. 17, No. 6, pp. 213-220, 2017. (In Persian).##
  13. Taheri, A., Omid, M., Ahmadi, H., Mohtasebi, S. “Giovanni. Maria Carlomagno, Intelligent fault diagnosis of cooling radiator based on thermal image processing and artificial intelligence techniques”, J Modares Mech Eng, Vol. 17, No. 2, pp. 240-250, 2017. (In Persian).##
  14. Gonzalez, R., Woods, C. “Digital Image Processing”; Prentice-Hall Inc, No. 2, pp. 120-160, 2002.##
  15. Sadeghi, M., Shafiee, M., Memarzadeh, Zavareh, Z., Memarzadeh Zavareh, F. “Using Image Processing in Grading Tile With Gabor Wavelet”; Int. Conf. Comp. Sci. Net. Tech. (ICCSNT), Changchun, China, 2012.##
  16. Danesh, M., Danesh, S., Khalili, Kh. “Multi-Sensory Data Fusion System for Tool Condition Monitoring Using Optimized Artificial Fuzzy Inference System”, Sci. J. Manag. Sys. Vol. 15, No 2, pp. 103-118, 2019.##