Method for Orientation Determining of the Detail for the Automated Soldering Technological Process

Purpose of reseach. Development of a technique for determining the orientation of parts during automatic soldering of a contact group based on the assembly of a combined image recognition systems.

Methods. Analysis of the technological process of assembling a part and evaluating the possibility of using various technologies for orienting workpieces. Development of a consistent technique for orienting a part, including the stage of recognizing a rounded edge using a vision system. Development of an algorithm for recognizing the orientation of the workpiece directly from the image of the part and along the contour of the shadow of the part. Setting up full-scale experiments on a test bench, obtaining numerical values for the accuracy of recognizing the orientation of a part for various algorithms.

Methodology. To solve the problem of transportation and positioning of the studied parts, methods of movement are used, due to controlled vibration inside the developed system of guides and cutters. To determine the rounded edge, image processing and recognition methods are used: the k-means method  for clustering the original image, the Hough transform for contour search, etc.

Results. In the course of the study, two algorithms for extracting details from the original image and the image of the shadows cast by the detail were developed. In the detection of an increased risk, more than 96%, however, when detecting sensitivity along the contour of the shadow, more than 99% were detected.

Conclusion. The technique for determining details developed within the framework of the work, including the stages of preliminary sorting according to the task of vibrotransportation, the stage of searching for a round face using a technical perspective system, including the contour of shadow details, makes it possible to obtain high accuracy even when assembling video equipment with a low level of detection.

1. Aletdinova A. A. et al. Tsifrovaya transformatsiya ekonomiki i promyshlennosti: problemy i perspektivy [Digital transformation of the economy and industry: problems and prospects]. Saint-Petersburg. 2017.

2. Plotnikov V. A. Tsifrovizatsiya proizvodstva: teoreticheskaya sushchnost' i perspektivy razvitiya v rossiiskoi ekonomike [Digitalization of production: theoretical essence and prospects for development in the Russian economy]. Izvestiya Sankt-Peterburgskogo gosudarstvennogo ekonomicheskogo universiteta = Bulletin of the St. Petersburg State University of Economics, 2018, no. 4 (112), pp. 16-24.

3. Ivanov D. A., Ivanova M. A., Sokolov B. V. Analiz tendentsii izmeneniya printsipov upravleniya predpriyatiyami v usloviyakh razvitiya tekhnologii Industrii 4.0 [Analysis of trends in changing the principles of enterprise management in the context of the development of Industry 4.0 technologies]. Informatika i avtomatizatsiya = Informatics and Automation, 2018, vol. 5, no. 60, pp. 97-127.

4. Bahrin M. A. K. et al. Industry 4.0: A review on industrial automation and robotic. Jurnal teknologi, 2016, vol.78, pp.6-13. https://doi.org/10.11113/jt.v78.9285.

5. Zozulya D. M. Tsifrovizatsiya rossiiskoi ekonomiki i Industriya 4. 0: vyzovy i perspektivy [Digitalization of the Russian economy and Industry 4. 0: challenges and prospects]. Voprosy innovatsionnoi ekonomiki = Issues of innovative economics, 2018, vol. 8, no. 1, pp. 1-14.6.

6. Shatalova N. I. Psikhofiziologicheskie osobennosti trudovogo povedeniya rabotnika [Psychophysiological features of the worker's labor behavior]. Vestnik NGUEU = Bulletin of the National State University of Economics, 2015, no. 1, pp. 178-189.

7. Danilova N. N. Psikhofiziologiya [Psychophysiology]. Moscow, Aspect Press Publ., 2012.

8. Sorokin G. A., Shilov V. V. Otsenka godovogo prirosta riska narusheniya zdorov'ya rabotnikov pri vysokoi intensivnosti truda [Evaluation of the annual increase in the risk of health disorders of workers at high labor intensity]. Gigiena i sanitariya = Hygiene and Sanitation, 2020, vol. 99, no. 6, pp. 618-623.

9. Tsvetaev S. S., Logachev K. I. Aktual'nye problemy avtomatizatsii promyshlennykh predpriyatii [Actual problems of automation of industrial enterprises]. Vestnik Belgorodskogo gosudarstvennogo tekhnologicheskogo universiteta im. V.G. Shukhova = Bulletin of the Belgorod State Technological University V.G. Shukhova, 2012, no. 1, pp. 87-89.

10. Malchikov A.V. et al. Analiz effektivnosti primeneniya ekzoskeleta v proizvodstvennom protsesse promyshlennogo predpriyatiya [Analysis of the effectiveness of the use of an exoskeleton in the production process of an industrial enterprise]. Yunost' i znaniya-garantiya uspekha = Youth and knowledge is a guarantee of success-2019. Kursk, 2019, pp. 234-237.

11. Vyatkin V. Software engineering in industrial automation: State-of-the-art review. IEEE Transactions on Industrial Informatics, 2013, vol. 9, no. 3, pp. 1234-1249. https://doi.org/10.1109/TII.2013.2258165

12. Xu H. et al. A survey on industrial Internet of Things: A cyber-physical systems perspective. Ieee access, 2018, vol. 6, pp. 78238-78259. https://doi.org/10.1109/ACCESS.2018.2884906

13. Jatsun S., Malchikov A., Yatsun A. Automatization of manual labor by using an industrial exoskeleton. 2020 International Russian Automation Conference (RusAutoCon). IEEE, 2020, pp. 470-475. https://doi.org/10.1109/RusAutoCon49822.2020.9208173

14. Malchikov A. et al. Control features of the electromechanical system with endeffector considering the regulated torque. MATEC Web of Conferences. EDP Sciences, 2017, vol. 113, pp. 02001. https://doi.org/10.1051/matecconf/201711302001

15. Jatsun S., Malchikov A., Yatsun A. Adaptive Control System for DC Electric Drive under Uncertainty. 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2020, pp. 1-5. https://doi.org/10.1109/ICIEAM48468.2020.9111876

16. Hossain M. Z. et al. A dynamic K-means clustering for data mining. Indonesian Journal of Electrical engineering and computer science, 2019, vol. 13, no. 2, pp. 521-526.

17. Sinaga K. P., Yang M. S. Unsupervised K-means clustering algorithm. IEEE access. 2020, vol. 8, pp. 80716-80727. https://doi.org/10.1109/ACCESS.2020.2988796

18. Chandan G. et al. Real time object detection and tracking using Deep Learning and OpenCV. 2018 International Conference on inventive research in computing applications (ICIRCA). IEEE, 2018, pp. 1305-1308. https://doi.org/10.1109/ICIRCA.2018.8597266

19. Wang J., Fu P., Gao R. X. Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 2019, vol. 51, pp. 52-60. https://doi.org/10.1016/j.jmsy.2019.03.002