| ArticleName |
Software service for quality control of assembly works |
| ArticleAuthorData |
Empress Catherine II Saint Petersburg Mining University, St. Petersburg, Russia
N. I. Koteleva, Associate Professor, Chair for Automation of Technological Processes and Production, Candidate of Engineering Sciences, e-mail: Koteleva_NI@pers.spmi.ru V. V. Valnev, Postgraduate Student, Chair for Automation of Technological Processes and Production
Hakel, St. Petersburg, Russia
V. V. Markov, Deputy General Director |
| Abstract |
Manual assembly of various components is currently widespread. A software service is presented that allows for real-time monitoring of electrical components assembly operations, identifying product defects, and automatically counting assembled parts. The software service allows for increasing the efficiency of assembly processes by reducing defects, reducing errors in process planning, and reducing the influence of the human factor on assembly processes. The stages of software service development and testing of its performance at the enterprise for manufacturing electrical products, JSC Hakel, are shown. The software service operation and calculation of its performance efficiency are shown using the example of four process operations for assembling surge protection devices. The versatility of the developed software service and its applicability for any process operation performed manually at non-ferrous metallurgy enterprises are shown. An assessment is made of the possibility of using the presented research results during installation of equipment and various automation systems, which is relevant for work on the implementation of JSC SoyuzTsMA developments during installation of valves, samplers, and installation of valve control units. The feasibility of implementing the software service during installation of vibration sensors and a microphone device of the VAZM-1M vibroacoustic mill loading analyzer is noted, since this ensures repeatability of the installation of sensors and a microphone after mill repairs and reduces the time for recalibration of the analyzer. |
| References |
1. Khokhlov S., Abiev Z., Makkoev V. The choice of optical flame detectors for automatic explosion containment systems based on the results of explosion radiation analysis of methane- and dust-air mixtures. Appl. Sci. 2022. Vol. 12. 1515. DOI: 10.3390/app12031515 2. Romashev A. O., Nikolaeva N. V., Gatiatullin B. L. Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition. Journal of Mining Institute. 2022. Vol. 256. pp. 677–685. DOI: 10.31897/PMI.2022.77 3. Cherepovitsyn A. E., Tretyakov N. A. Development of a new assessment system for the applicability of digital projects in the oil and gas sector. Journal of Mining Institute. 2023. Vol. 262. pp. 628–642. 4. Korolev N., Kozyaruk A., Morenov V. Efficiency increase of energy systems in oil and gas industry by evaluation of electric drive lifecycle. Energies. 2021. Vol. 14. 6074. DOI: 10.3390/en14196074 5. Boykov A. V., Payor V. A. Machine vision system for monitoring the process of levitation melting of non-ferrous metals. Tsvetnye Metally. 2023. No. 4. pp. 85–89. 6. Sannikov D. O., Orlov I. V., Berveno A. V., Gavrilov A. I. A review of innovative development areas, novel materials and research techniques in metallurgical industry. Tsvetnye Metally. 2023. No. 9. pp. 8–13. 7. Zhukovsky Yu. L., Suslikov P. K. Evaluation of the potential effect of applying demand management technology at mining enterprises. Ustoychivoe razvitie gornykh territoriy. 2024. Vol. 16. No. 3. pp. 895–908. DOI: 10.21177/1998-4502-2024-16-3-895-908 8. Shishlyannikov D. I., Zverev V. Yu., Zvonareva A. G., Frolov S. A., Ivanchenko A. A. Evaluation of energy efficiency of operation and increase in the operating time of hydraulic drives of sucker rod borehole pump units under difficult operating conditions. Zapiski Gornogo instituta. 2023. Vol. 261. pp. 349–362 9. Myakotnykh A. A., Ivanova P. V., Ivanov S. L. Criteria and technological requirements for the creation of a bridge platform for the extraction of peat raw materials for climate-neutral geotechnology. Gornaya promyshlennost. 2024. No. 4. pp. 116–120. DOI: 10.30686/1609-9192-2024-4-116-120 10. Grendel H. et al. Enabling manual assembly and integration of aerospace structures for Industry 4.0 – methods. Procedia Manufacturing. 2017. Vol. 14. pp. 30–37. DOI: 10.1016/j.promfg.2017.11.004 11. Ashourpour M. Automation of operations in assembly of battery modules in electric vehicles. IFAC-PapersOnLine. 2024. Vol. 58, Iss. 19. pp. 754–759. DOI: 10.1016/j.ifacol.2024.09.212 12. Sudhoff M. et al. Objective data acquisition as the basis of digitization in manual assembly systems. Procedia CIRP. 2020. Vol. 93. pp. 1176–1181. DOI: 10.1016/j.procir.2020.03.032 13. Phoulady A. et al. Synthetic data augmentation to enhance manual and automated defect detection in microelectronics. Microelectronics Reliability. 2023. Vol. 150. 115220. DOI: 10.1016/j.microrel.2023.115220 14. Stoianova A. D., Trofimets V. Ya., Matrokhina K. V. Methodological approach to rating of arctic zone companies based on ESG indicators. MIAB. 2024. No. 6. pp. 149–162. DOI: 10.25018/0236_1493_2024_6_0_149 15. Miqueo A., Torralba M., Yagüe-Fabra J. A. Models to evaluate the performance of high-mix low-volume manual or semi-automatic assembly lines. Procedia CIRP. 2022. Vol. 107. pp.1461–1466. DOI: 10.1016/j.procir.2022.05.175
16. Ohlig J. et al. Human-centered performance management in manual assembly. Procedia CIRP. 2021. Vol. 97. pp.418–422. DOI: 10.1016/j.procir. 2020.05.261 17. Fink K. et al. Dynamic value stream optimization for manual assembly in the learning factory for cyber-physical production systems. Procedia Manufacturing. 2020. Vol. 45. pp. 78–83. DOI: 10.1016/j.promfg.2020.04.070 18. Hemono P., Nait Chabane A., Sahnoun M. Multi objective optimization of human–robot collaboration: A case study in aerospace assembly line. Computers & Operations Research. 2025. Vol. 174. 106874. DOI: 10.1016/j.cor.2024.106874 19. Nevskaya, M., Shabalova, A., Kosovtseva, T., Nikolaychuk, L. Applications of simulation modeling in mining project risk management: criteria, algorithm, evaluation. Journal of Infrastructure, Policy and Development. 2024. Vol. 8, Iss. 8. 5375. DOI: 10.24294/jipd.v8i8.5375 20. Conrad J. et al. Deep learning-based error recognition in manual cable assembly using synthetic training data. Procedia CIRP. 2024. Vol. 128. pp. 239–244. DOI: 10.1016/j.procir.2024.04.005 21. Klages B., Zaeh M. Concept of a data-based approach for the prediction and reduction of human errors in manual assembly. Procedia CIRP. 2023. Vol. 116. pp. 209–214. DOI: 10.1016/j.procir.2023.02.036 22. Liu F., Li J. Application exploration of robot process automation in digital labor time management system. Procedia Computer Science. 2023. Vol. 228. pp. 89–97. DOI: 10.1016/j.procs.2023.11.012 23. Kim G.-Y. et al. Data-driven analysis and human-centric assignment for manual assembly production lines. Computers & Industrial Engineering. 2024. Vol. 188. 109896. DOI: 10.1016/j.cie.2024.109896 24. Wang J.-G. et al. A hierarchical granger causality analysis framework based on information of redundancy for root cause diagnosis of process disturbances. Computers & Chemical Engineering. 2024. Vol. 182. pp. 108589. DOI: 10.1016/j.compchemeng.2024.108589 25. Chen X., Ren J., Zhao C. Synergetic decomposition of input-output dependency for control system intelligent monitoring: a perspective from information theory. IFAC-PapersOnLine. 2024. Vol. 58, Iss. 4. pp. 312–317. DOI: 10.1016/j.ifacol.2024.07.236 26. Thurnheer J. et al. Manual data collection in assembly lines: a case study on the human factor in data accuracy. IFAC-PapersOnLine. 2024. Vol. 58, Iss. 19. pp. 85–90. DOI: 10.1016/j.ifacol.2024.09.098 27. Verdu C. R. et al.enhancing manual assembly training using mixed reality and virtual sensors. Procedia CIRP. 2024. Vol. 126. pp. 769–774. DOI: 10.1016/j.procir.2024.08.328 28. Chu C.-H., Ko C.-H. An experimental study on augmented reality assisted manual assembly with occluded components. Journal of Manufacturing Systems. 2021. Vol. 61. pp. 685–695. DOI: 10.1016/j.jmsy.2021.04.003 29. Yamashita T., Suzuki H., Tasaki R. Motion and force measurement of human fingertips during manual operation to achieve high-precision assembly by articulated robots. Measurement: Sensors. 2022. Vol. 24. 100413. DOI: 10.1016/j.measen.2022.100413 30. Keshvarparast A. et al. Integrating collaboration scenarios and workforce individualization in collaborative assembly line balancing. International Journal of Production Economics. 2025. Vol. 279. 109450. DOI: 10.1016/j.ijpe.2024.109450 31. Navas-Reascos G. E. et al. A cost-benefit analysis for a wire harness assembly workstation: Manual vs. collaborative workstation. Manufacturing Letters. 2023. Vol. 38. pp. 65–68. DOI: 10.1016/j.mfglet.2023.09.011 32. Daling L. M. et al. Assemble it like this! – Is AR- or VR-based training an effective alternative to video-based training in manual assembly? Applied Ergonomics. 2023. Vol. 110.104021. DOI: 10.1016/j.apergo.2023.104021 33. Fathi M. et al. Unveiling the potential of mixed reality: enhancing time measurement and operator support in manual assembly processes. Procedia Computer Science. 2024. Vol. 232. pp. 2670–2679. DOI: 10.1016/j.procs.2024.02.084 34. Puttero S. et al. Preliminary comparison between manual assembly and intelligent human-robot collaborative assemblies in terms of quality and assembly time. Procedia CIRP. 2024. Vol. 126. pp. 206–211. DOI: 10.1016/j.procir.2024.08.326 35. Papadopoulos G. et al. On intelligent object sorting and assembly: versatile end-effector for robotized handling of electrical components. Procedia CIRP. 2024. Vol. 128. pp. 363–368. DOI: 10.1016/j.procir.2024.07.051 |