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A computer vision system for chain link sorting application

Fatih Akkoyun, Merzun Niray Baş, Seher Şimşek, Duran Emre Uysal, Sinan Güçlüer, Adem Özçelik


Sorting is a necessary process in industrial mass production applications. Considering the increasing population and demand for consumer products, simpler means to perform image detection and analysis through machine vision applications are crucial. This study demonstrates an effective machine vision application by analysing the correct faces of chain links. In the chain fabrication process, the high throughput production line requires proper alignment of each chain link due to the tolerance difference of each linked face due to the machining procedure. The manual labour currently applied in correcting the face orientation of the chain links in the production flow line increases the fabrication cost and human error. Through a simple machine vision application and an industrial-grade global shutter camera, detection of continuously flowing chain links is achieved by using a marker. The procedure works by detecting the marker on the related face of the chain links through image thresholding and analysis. The study is offering 100% accuracy for sorting single and multi-line chain links in real-time applications. The demonstrated application can be coupled by a sorting mechanism adapted to various quality control and sorting requirements in industrial manufacturing.

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M. H. Ali, S. Kurokawa, and K. Uesugi, “Application of machine vision in improving safety and reliability for gear profile measurement,” Mach. Vis. Appl., 25(6), pp. 1549–1559, 2014.

N. Herakovic, M. Simic, F. Trdic, and J. Skvarc, “A machine-vision system for automated quality control of welded rings,” Mach. Vis. Appl., 22(6), pp. 967–981, 2011.

B. Aksoy, O. Salman, İ. Sayin, and K. Özsoy, “11 Artificial intelligence applications for medical diagnosis and production with 3D printing technologies,” in Artificial Intelligence for Data-Driven Medical Diagnosis, De Gruyter, 2021, pp. 225–244.

P. Balkir, K. Kemahlıoğlu, and U. Yücel, “Machine vision system: Food industry applications and practices,” Turkish J. Agric. - Food Sci. Technol., 7(7), p. 989, 2019.

E. Ukwatta, J. Samarabandu, and M. Hall, “Machine vision system for automated spectroscopy,” Mach. Vis. Appl., 23(1), pp. 111–121, 2012.

P. K. Sinha, “Review of image parameters,” in Image Acquisition and Preprocessing for Machine Vision Systems, 1000 20th Street, Bellingham, WA 98227-0010 USA: SPIE, pp. 601–638, 2012.

K. Özsoy, B. Aksoy, and M. Yücel, “Design and manufacture of continuous automatic 3D printing device with conveyor system by image processing technology,” Erzincan University Journal of Science and Technology, vol.13, p. 392-403, 2020.

L. Pérez, Í. Rodríguez, N. Rodríguez, R. Usamentiaga, and D. García, “Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review,” Sensors, 16(3), p. 335, 2016.

F. Akkoyun et al., “Measurement of micro burr and slot widths through image processing: comparison of manual and automated measurements in micro-milling,” Sensors, 21(13), p. 4432, 2021.

F. Akkoyun and A. Ozcelik, “Rapid characterization of cell and bacteria counts using computer vision,” Turkish Journal of Nature and Science, 10(1), pp.269-274, 2021.

T. D. Bhoite, P. M. Pawar, and B. D. Gaikwad, “Fea based study of effect of radial variation of outer link in A typical roller chain link assembly,” Int. J. Mech. Ind. Eng., no. 2231, pp. 69–74, 2012.

A. Güney, M. Temizkan, S. Tekin, D. C. Samuk, and O. Çakır, “Temperature control of an electric furnace with intuitive control methods,” Turk. J. of Electromec. & Energy, 5(1), pp. 3-8, 2020.

M. N. Örnek and H. Hacıseferoğulları, “ Design of real time image processing machine for carrot classification,” Yüzüncü Yıl University Journal of Agricultural Sciences, 30(2), pp. 355–366, 2020.

D. Martin, D. M. Guinea, M. C. García-Alegre, E. Villanueva, and D. Guinea, “Multi-modal defect detection of residual oxide scale on a cold stainless steel strip,” Mach. Vis. Appl., 21(5), pp. 653–666, 2010.

A. R. Jiménez, R. Ceres, and J. L. Pons, “A vision system based on a laser range-finder applied to robotic fruit harvesting,” Mach. Vis. Appl., 11(6), pp. 321–329, 2000.

Z. Ren, J. Liao, and L. Cai, “An algorithm to estimate the crown patterns of diamonds based on machine vision,” Mach. Vis. Appl., 23(2), pp. 197–215, 2012.

M. F. Carlsohn, “Spectral image processing in real-time,” J. Real-Time Image Process., 1(1), pp. 25–32, 2006.

A. Ozcelik, “Atomic Layer Deposition (ALD) of Vanadium Oxide thin films,” Turkish J. Electromechanics Energy, 4(2), pp. 13–18, 2019.

F. Murtagh et al., “A machine vision approach to the grading of crushed aggregate,” Mach. Vis. Appl., 16(4), pp. 229–235, 2005.

B. Büyükarıkan and E. Ülker, “Aydınlatma Özniteliği Kullanılarak Evrişimsel Sinir Ağı Modelleri ile Meyve Sınıflandırma,” Uludağ Univ. J. Fac. Eng., 25(1), pp. 81–100, 2020.

O. Ağın and A. Taner, “Determination of weed intensity in wheat production using image processing techniques,” ANADOLU J. Agric. Sci., 30(2), p. 110, 2015.

G. Cagıl and B. Yıldırım, “Detection of an assembly part with deep learning and image processing,” J. Intell. Syst. Theory Appl., 3(2), pp. 31–37, 2020.


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