Manufacturing Technology 2021, 21(1):141-148 | DOI: 10.21062/mft.2021.018

Industrial Automatic Assembly Technology Based on Machine Vision Recognition

Shiqian Xiang
School of Art, Gansu University of Political Science and Law, Lanzhou, Gansu 730070, China.

With the rapid development of science and technology, the means of industrial production have become more diversified and intelligent, and the development of new means of industrial production has become an increasingly important research topic. Therefore, the automatic assembly technology was studied taking machine vision system as the main research subject in this study. An automated assembly model of industrial technology based on machine vision recognition was established, parameters such as the part positioning parameter, assembly time, the number of parts wrongly assembled and the number of parts missing and the qualification rate of assembly were obtained, and the corresponding experimental conclusions were obtained. Moreover it was compared with the traditional manual assembly technology, and it was found that the automatic assembly technology based on machine vision recognition had better performance and more remarkable experimental results compared with the traditional manual assembly technology, and the traditional manual assembly technology needed continuous modification and optimization. This work provides a new route for automatic assembly technology in industrial technology.

Keywords: Machine vision, Recognition, Industrial technology, Automated assembly

Received: August 21, 2020; Revised: January 22, 2021; Accepted: January 27, 2021; Prepublished online: February 10, 2021; Published: February 24, 2021  Show citation

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Xiang S. Industrial Automatic Assembly Technology Based on Machine Vision Recognition. Manufacturing Technology. 2021;21(1):141-148. doi: 10.21062/mft.2021.018.
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    References

    1. CHAWLA, V. K., CHANDA, A. K., ANGRA, S. (2018). A Clonal Selection Algorithm for Minimizing Dis-tance Travel and Back Tracking of Automatic Guided Vehicles in Flexible Manufacturing System. In: Jour-nal of The Institution of Engineers (India): Series C, pp. 1-10. Go to original source...
    2. CHAUHAN, V., SURGENOR, B. (2015). A Comparative Study of Machine Vision Based Methods for Fault Detection in an Automated Assembly Machine. In: Procedia Manufacturing, Vol. 1, pp. 416-428. Go to original source...
    3. CHAUHAN, V., SURGENOR, B. (2017). Fault detection and classification in automated assembly machines using machine vision. In: The International Journal of Advanced Manufacturing Technology, Vol. 90, No. 9-12, pp. 2491-2512. Go to original source...
    4. ZORCOLO, A., ESCOBAR-PALAFOX, G., GAULT, R., SCOTT, R., RIDGWAY, K. (2011). Study of light-ing solutions in machine vision applications for automated assembly operations. Go to original source...
    5. CHAUHAN, V., SURGENOR, B. (2016). Performance evaluation of MVI for fault detection in automated assembly machines. In: 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Go to original source...
    6. ODENTHAL, B., MAYER, M. P., KABU,ß W., SCHLICK, M. (2012). Design and evaluation of an Aug-mented Vision System for human-robot cooperation in cognitively automated assembly cells. In: Interna-tional Multi-conference on Systems. Go to original source...
    7. JIANG, H., DUERSTOCK, B. S., WACHS, J. P. (2014). A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 44, No. 5, pp. 630-641. Go to original source...
    8. CUBERO, S., LEE, W. S., ALEIXOS, N., ALBERT, F., BLASCO, J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest-a review. In: Food and Bioprocess Technology, Vol. 9, No. 10, pp. 1623-1639. Go to original source...
    9. SCHMITT, R., TOBIAS, F., ABBAS, B., ABEL, P., KIMMELMANN, W., KOSSE, P., BURATTI, A. (2016). Automation, Communication and Cybernetics in Science and Engineering 2015/2016. Springer International Publishing.
    10. ARDHY, F., HARIADI, F. I. (2017). Development of SBC-based machine-vision system for PCB board assembly Automatic Optical Inspection. In: 2016 International Symposium on Electronics and Smart Devices (ISESD). Go to original source...
    11. BASKORO, A. S., TANDIAN, R., HAIKAL, EDYANTO, A., SARAGIH, A. S. (2017). Automatic Tung-sten Inert Gas (TIG) welding using machine vision and neural network on material SS304. In: International Conference on Advanced Computer Science and Information Systems. Go to original source...
    12. COELHO, P. A., TORRES, S. N., RAMÍREZ, W. E., GUTIÉRREZB, P. A., TORO, C. A., SOTO, J. G., SBARBAROA, D. G., PEZOAAB, J. E. (2016). A machine vision system for automatic detection of para-sites Edotea magellanica in shell-off cooked clam Mulinia edulis. In: Journal of Food Engineering, Vol. 181, pp. 84-91. Go to original source...
    13. QUIROS, A. R. F., ABAD, A., BEDRUZ, R. A., UY, A. C., DADIOS, E. P. (2016). A genetic algorithm and artificial neural network-based approach for the machine vision of plate segmentation and character recognition. In: International Conference on Humanoid. Go to original source...
    14. DUTTA GUPTA, S., KARMAKAR, A. (2017). Machine vision based evaluation of impact on light emitting diodes (LEDs) on shoot regeneration and the effect of spectral quality on phenolic content and antioxidant capacity in, Swertia chirata. In: Journal of Photochemistry and Photobiology B: Biology, Vol. 174, pp. 162-172. Go to original source...
    15. AL-NAJI, A., CHAHL, J. (2017). Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System with Noise Artifact Removal. In: IEEE Journal of Translational Engineering in Health and Medicine. Go to original source...
    16. NOURI-AHMADABADI, H., OMID, M., MOHTASEBI, S., FIROUZ, M. S. (2017). Design, develop-ment and evaluation of an online grading system for peeled pistachios equipped with machine vision tech-nology and support vector machine. In: Information Processing in Agriculture, pp. S2214317316300117. Go to original source...
    17. WANG, X. D. (2015). The precision measurement and assembly for miniature parts based on double ma-chine vision systems. In: International Symposium on Precision Engineering Measurement & Instrumentation. Interna-tional Society for Optics and Photonics. Go to original source...
    18. SHI, C., QIAN, J., HAN, S., FAN, B., YANG, X., WU, X. (2018). Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia ( Oreochromis niloticus ) pupil and gill color during storage at 4 °C. In: Food Chemistry, Vol. 243, pp. 134-140. Go to original source...
    19. BALABANTARAY, B. K., DAS, B., BISWAL, B. (2014). Comparison of edge detection algorithms for part identification in a vision guided robotic assembly system. In: Studies in Computational Intelligence, Vol. 543, pp. 183-206. Go to original source...
    20. JIANG, S., LIU, P., FU, D., XUE, Y. M., LUO, W. T., WANG, M. J. (2017). A low-cost rapid upper limb evaluation method in manual assembly line based on somatosensory interaction technology. In: 2015th In-ternational Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation (CDMMS 2017). AIP Pub-lishing LLC. Go to original source...
    21. WU, Y., LIN, S., DAI, L., LUO, L. (2017). Dynamic balancing for mixing assembly line based on PSO - GA cooperative optimization. In: Manufacturing Technology, Vol. 17, No. 4, pp. 622-628. Go to original source...
    22. LIU, Q., YAN, Y. R., CAO, L. L. (2016). Computer visual measurement technology and algorithm simulation for the assembly of large aircraft parts. In: Manufacturing Technology, Vol. 16, No. 3, pp. 538-543 Go to original source...

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