The purpose of the study was to investigate the efficiency of automated welding processes in the restoration of pipelines of power facilities, with an emphasis on improving reliability and reducing repair time. The study used the analysis of technical literature, practical cases and the results of experimental implementation of automated welding technologies at power facilities. It was established that automated welding processes ensure high quality of welded joints in pipelines of power facilities, which significantly reduces the likelihood of defects. It was confirmed that the use of modern welding technologies helps to increase the corrosion resistance of joints and their ability to withstand high loads. Process automation has proven effective in ensuring stable welding performance, even under difficult operating conditions. It turned out that automated systems reduce the impact of the human factor, increasing the safety and reliability of work. The study also showed that automated technologies contribute to the rational use of energy resources during welding, minimise the cost of operating equipment, and ensure compliance of restored pipelines with modern technical and environmental standards. The introduction of these technologies helps to optimise repair work, reduce their duration and increase the durability of pipelines. This confirmed the feasibility of using automated welding processes to restore the infrastructure of energy facilities. It was determined that automated processes can ensure high welding accuracy, which is critical for the operation of pipelines under high pressure. The study showed that such technologies help to reduce material consumption by minimising the number of defects. In addition, the introduction of automation has proven to be an effective tool for improving the productivity of repair work and ensuring stable operation of energy systems in the long term
corrosion resistance, high loads, human factor, safety, energy resources, environmental standard
[1] Alarcón, M., Martínez-García, F.M., & de León Hijes, F.C. (2021). Energy and maintenance management systems in the context of industry 4.0. Implementation in a real case. Renewable and Sustainable Energy Reviews, 142, article number 110841. doi: 10.1016/j.rser.2021.110841.
[2] Andrienko, O., & Tsybulskiy, L. (2024). Simulation of gas flow through a perforated pipe in a coaxial discharge system. Technologies and Engineering, 25(5), 9-16. doi: 10.30857/2786-5371.2024.5.1.
[3] Bagheri, B., Abbasi, M., & Hamzeloo, R. (2021). Comparison of different welding methods on mechanical properties and formability behaviors of tailor welded blanks (TWB) made from AA6061 alloys. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 235(12), 2225-2237. doi: 10.1177/0954406220952504.
[4] Berx, N., Decré, W., Morag, I., Chemweno, P., & Pintelon, L. (2022). Identification and classification of risk factors for human-robot collaboration from a system-wide perspective. Computers & Industrial Engineering, 163, article number 107827. doi: 10.1016/j.cie.2021.107827.
[5] Buang, A.S., Bakar, M.S., & Rohani, M.Z. (2024). A review of trend advanced welding process and welding technology in industries. International Journal of Technical Vocational and Engineering Technology, 5(1), 133-145.
[6] Bulganbayev, M.A., Suliyev, R., & Fonseca Ferreira, N.M. (2024). Prototype for the application of production of heavy steel structures. Electronics, 13(2), article number 387. doi: 10.3390/electronics13020387.
[7] Chakradhar, R., Ortega-Moody, J., Jenab, K., & Moslehpour, S. (2022). Improving the quality of welding training with the help of mixed reality along with the cost reduction and enhancing safety. Management Science Letters, 12(4), 321-330. doi: 10.5267/j.msl.2022.4.002.
[8] Chukwunweike, J., Anang, A.N., Adeniran, A.A., & Dike, J. (2024). Enhancing manufacturing efficiency and quality through automation and deep learning: Addressing redundancy, defects, vibration analysis, and material strength optimization. World Journal of Advanced Research and Reviews, 23(3), 1272-1295. doi: 10.30574/wjarr.2024.23.3.2800.
[9] Curiel, D., Veiga, F., Suarez, A., & Villanueva, P. (2023). Advances in robotic welding for metallic materials: Application of inspection, modeling, monitoring and automation techniques. Metals, 13(4), article number 711. doi: 10.3390/met13040711.
[10] Doroshenko, Ya., Stetsiuk, S., Bondarenko, R., & Daniv, Z. (2023). Technologies for trenchless renovation of pipeline systems. Prospecting and Development of Oil and Gas Fields, 23(2), 17-32. doi: 10.31471/1993-9973-2023-2(87)-17-32.
[11] Faccio, M., Granata, I., Menini, A., Milanese, M., Rossato, C., Bottin, M., Minto, R., Pluchino, P., Gamberini, L., Boschetti, G., & Rosati, G. (2023). Human factors in cobot era: A review of modern production systems features. Journal of Intelligent Manufacturing, 34(1), 85-106. doi: 10.1007/s10845-022-01953-w.
[12] Gbagba, S., Maccioni, L., & Concli, F. (2023). Advances in machine learning techniques used in fatigue life prediction of welded structures. Applied Sciences, 14(1), article number 398. doi: 10.3390/app14010398.
[13] Guo, Q., Yang, Z., Xu, J., Jiang, Y., Wang, W., Liu, Z., Zhao, W., & Sun, Y. (2024). Progress, challenges and trends on vision sensing technologies in automatic/intelligent robotic welding: State-of-the-art review. Robotics and Computer-Integrated Manufacturing, 89, article number 102767. doi: 10.1016/j.rcim.2024.102767.
[14] Hart, L., Knoblach, S., & Möser, M. (2023). Automation strategies for the photogrammetric reconstruction of pipelines. Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 91(4), 313-334. doi: 10.1007/s41064-023-00244-0.
[15] Hicks, J., Kaushal, V., & Jamali, K. (2022). A comparative review of trenchless cured-in-place pipe (CIPP) with spray applied pipe lining (SAPL) renewal methods for pipelines. Frontiers in Water, 4, article number 904821. doi: 10.3389/frwa.2022.904821.
[16] Hrinchenko, H., Koval, V., Shmygol, N., Sydorov, O., Tsimoshynska, O., & Matuszewska, D. (2023). Approaches to sustainable energy management in ensuring safety of power equipment operation. Energies, 16(18), article number 6488. doi: 10.3390/en16186488.
[17] Hrytsanchuk, A., Hrytsanchuk, V., Riabko, H., Semysiuk, O., & Stanetskyi, A. (2023). Integrated approaches to corrosion risk management in industrial pipelines. Prospecting and Development of Oil and Gas Fields, 23(3), 7-14. doi: 10.69628/pdogf/3.2023.07.
[18] Iqbal, M., Karuppanan, S., Perumal, V., Ovinis, M., & Rasul, A. (2023). Rehabilitation techniques for offshore tubular joints. Journal of Marine Science and Engineering, 11(2), article number 461. doi: 10.3390/jmse11020461.
[19] Iskandarov, E.X.O., & Baghirov, S.A.O. (2022). Analytical and wave-depression methods of elimination of the onset of hydration in subsea gas pipelines. News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences, 2022(4), 96-108. doi: 10.32014/2022.2518-170X.203.
[20] Ismayilov, G.G., Iskenderov, E.K., & Ismayilova, F.B. (2021). Problems of Hydrodynamic Corrosion in Multiphase Pipelines. Protection of Metals and Physical Chemistry of Surfaces, 57(1), 147-152. doi: 10.1134/S2070205121010123.
[21] Ismayilov, G.G., Iskenderov, É.K., Ismayilova, F.B., & Zeinalova, G.A. (2020). Controlled Methods to Suppress Pressure Pulsations in Multiphase Pipelines. Journal of Engineering Physics and Thermophysics, 93(1), 216-222. doi: 10.1007/s10891-020-02111-w.
[22] Kahnamouei, J.T., & Moallem, M. (2024). Advancements in control systems and integration of artificial intelligence in welding robots: A review. Ocean Engineering, 312, article number 119294. doi: 10.1016/j.oceaneng.2024.119294.
[23] Karim, M.A., Manladan, S.M., Afroz, H.M., Jin, W., Krishna, T., Ji, C., Kim, D.B., & Park, Y.-D. (2023). Critical effect of heat input on joint quality in resistance element welding of Al and steel. Journal of Manufacturing Processes, 95, 91-104. doi: 10.1016/j.jmapro.2023.04.005.
[24] Kou, S. (2003). Welding metallurgy (2nd ed.). Hoboken: John Wiley & Sons.
[25] Li, Y., Geng, S., Shu, L., Li, Y., & Jiang, P. (2023). Ultra-high-power laser welding of thick-section steel: Current research progress and future perspectives. Optics & Laser Technology, 167, article number 109663. doi: 10.1016/j.optlastec.2023.109663.
[26] Liu, J., Jiao, T., Li, S., Wu, Z., & Chen, Y.F. (2022a). Automatic seam detection of welding robots using deep learning. Automation in Construction, 143, article number 104582. doi: 10.1016/j.autcon.2022.104582.
[27] Liu, Q., Chen, C., & Chen, S. (2022b). Key technology of intelligentized welding manufacturing and systems based on the internet of things and multi-agent. Journal of Manufacturing and Materials Processing, 6(6), article number 135. doi: 10.3390/jmmp6060135.
[28] Loukas, C., Williams, V., Jones, R., Vasilev, M., MacLeod, C.N., Dobie, G., Sibson, J., Pierce, S.G., & Gachagan, A. (2021). A cost-function driven adaptive welding framework for multi-pass robotic welding. Journal of Manufacturing Processes, 67, 545-561. doi: 10.1016/j.jmapro.2021.05.004.
[29] Mahto, R.P., & Rout, M. (2024). Challenges and prospects of welding 4.0 adoption: Implication for emerging economics. In B.Ch. Behera, B.R. Moharana, K. Muduli & S.M. Islam (Eds.), Smart technologies for improved performance of manufacturing systems and services (pp. 71-93). Boca Raton: CRC Press. doi: 10.1201/9781003346623.
[30] Mehta, A., & Vasudev, H. (2024). Advances in welding sensing information processing and modelling technology: An overview. Journal of Adhesion Science and Technology. doi: 10.1080/01694243.2024.2388141.
[31] Mishra, D., Pal, S.K., & Chakravarty, D. (2021). Industry 4.0 in welding. In J.P. Davim (Ed.), Welding technology (pp. 253-298). Cham: Springer. doi: 10.1007/978-3-030-63986-0_8.
[32] Mishra, R.S., & Ma, Z.Y. (2005). Friction stir welding and processing. Materials Science and Engineering: R: Reports, 50(1-2), 1-78. doi: 10.1016/j.mser.2005.07.001.
[33] Naik, D.K., Sharma, V.P., & Dinesh Kumar, R. (2024). Automation in welding industries. In S.Q. Moinuddin, S.H. Saheb, A.K. Dewangan, M.M. Cheepu & S. Balamurugan (Eds.), Automation in welding industry: Incorporating artificial intelligence, machine learning and other technologies (pp. 37-48). Beverly: Scrivener Publishing LLC. doi: 10.1002/9781394172948.ch3.
[34] Naranjo, J.E., Caiza, G., Velastegui, R., Castro, M., Alarcon-Ortiz, A., & Garcia, M. V. (2022). A scoping review of pipeline maintenance methodologies based on industry 4.0. Sustainability, 14(24), article number 16723. doi: 10.3390/su142416723.
[35] Nguyen, T.-T., Nguyen, C.-T., & Van, A.-L. (2023). Sustainability-based optimization of dissimilar friction stir welding parameters in terms of energy saving, product quality, and cost-effectiveness. Neural Computing and Applications, 35(7), 5221-5249. doi: 10.1007/s00521-022-07898-8.
[36] Prodous, O., Shlychkov, D., Abrosimova, I., & Chelonenko, A. (2024). Technical condition of pipeline systems in operation. E3S Web of Conferences, 583, article number 03012. doi: 10.1051/e3sconf/202458303012.
[37] Ren, Y., & Skilton, R. (2024). A review of pipe cutting, welding, and NDE technologies for use in fusion devices. Fusion Engineering and Design, 202, article number 114396. doi: 10.1016/j.fusengdes.2024.114396.
[38] Said, M.A., You, S.K., Khamis, N.K., Sabri, M.A., Ismail, A.R., & Ardiyanto, A. (2023). Relationship between environmental stress factors and worker performance under welding job activity. Journal of Advanced Manufacturing Technology, 17(3), 41-54.
[39] Sazonova, S.A., Nikolenko, S.D., Osipov, A.A., Zyazina, T.V., & Venevitin, A.A. (2021). Weld defects and automation of methods for their detection. Journal of Physics: Conference Series, 1889, article number 022078. doi: 10.1088/1742-6596/1889/2/022078.
[40] Schumacher, S., Hall, R., Waldman-Brown, A., & Sanneman, L. (2022). Technology adoption of collaborative robots for welding in small and medium-sized enterprises: A case study analysis. In Proceedings of the conference on production systems and logistics: CPSL 2022 (pp. 462-471). Hannover: Publish-Ing. doi: 10.15488/12176.
[41] Senthil, S.M., Bhuvanesh Kumar, M., & Dennison, M.S. (2022). A contemporary review on friction stir welding of circular pipe joints and the influence of fixtures on this process. Advances in Materials Science and Engineering, 2022(1), article number 1311292. doi: 10.1155/2022/1311292.
[42] Valovoi, О., Grytsaienko, О., & Popruha, D. (2022). Technical state assessment of the supporting structures of the technical buildings. Journal of Kryvyi Rih National University, 20(2), 113-127. doi: 10.31721/2306-5451-2022-1-55-123-127.
[43] Zhang, X., Hu, X., Li, H., Zhang, Z., Chen, H., & Sun, H. (2024). Research on predicting welding deformation in automated laser welding processes with an enhanced DEWOA-BP algorithm. Machines, 12(5), article number 307. doi: 10.3390/machines12050307.