Machinery & Energetics

Use of robotic and automated systems in welding and restoration of parts

Iryna Zakharova, Serhii Shchetynin, Vira Shchetynina, Anton Zusin, Iryna Volenko
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Abstract

The research was aimed at analysing the use of robotic systems in welding and surfacing processes of mechanical engineering to increase the productivity, accuracy, and economic efficiency of production operations. The study used methods of comparative analysis, economic analysis, and risk analysis to assess the effectiveness of implementing robotic systems in the welding process and investigate their impact on costs, product quality, and working conditions. Analysis of the effectiveness of implementing robotic technologies has shown that automation of these processes reduces the likelihood of defects in welds and surfaced coatings, ensuring stable quality at all stages of production. The use of robotic systems such as ABB IRB 6700 and Fanuc ARC Mate 100iD allowed achieving precise control over welding and surfacing parameters, in particular, temperature, material feed rate, and current. Robotic welding and surfacing systems, in particular, from Fronius, KUKA and YASKAWA, are actively used in the energy, metallurgy, nuclear and aviation industries to improve the accuracy, safety, and efficiency of production processes. The study included the development of energy-saving technology for surfacing rolls of rolling mills to improve their wear resistance and corrosion resistance using Tesla Weld SAW/MMA MZ 630 devices and the Tesla Weld R1 welding robotic arm to automate the process and ensure high quality of the deposited layer. It was noted that robotic systems can work with a wide range of materials, in particular aluminium, steel, and alloys, which expands the possibilities of using the technology in mechanical engineering. Due to the robotisation of welding and surfacing processes, a significant reduction in production costs is achieved, since the need for manual control and correction of the process is reduced. Automation allows surfacing on worn parts, which extends the service life of the equipment and reduces the cost of replacing it

Keywords

product quality, automation, materials, safety, economic efficiency, competitiveness

Suggested citation
Zakharova, I., Shchetynin, S., Shchetynina, V., Zusin, A., & Volenko, I. (2025). Use of robotic and automated systems in welding and restoration of parts. Machinery & Energetics, 16(1), 117-129. https://doi.org/10.31548/machinery/1.2025.117
References

[1] Abebe, R., & Gopal, M. (2023). Optimization and simulation of arc welding robot parameters based on offline programming. Materials Today: Proceedings. doi: 10.1016/j.matpr.2023.02.291.

[2] Ajiga, D., Okeleke, P.A., Folorunsho, S.O., & Ezeigweneme, C. (2024). The role of software automation in improving industrial operations and efficiency. International Journal of Engineering Research Updates, 7(1), 22-35. doi: 10.53430/ijeru.2024.7.1.0031.

[3] Akberdin, A.A., Kim, A.S., Tolymbekova, L.B., & Sultangaziyev, R.B. (2023). Development of technology for producing complex boron-containing aluminum-silicon ferroalloy. Metallurgist, 67(5-6), 749-756. doi: 10.1007/s11015-023-01562-9.

[4] Ali, M., Alkaabi, A.K., & Lee, J.I. (2022). CFD simulation of an integrated PCM-based thermal energy storage within a nuclear power plant connected to a grid with constant or variable power demand. Nuclear Engineering and Design, 394, article number 111819. doi: 10.1016/j.nucengdes.2022.111819.

[5] Aydinliyim, T., Çil, E.B., & Murthy, N.N. (2023). Input material reduction incentives versus scrap recycling for closed‐loop supply chains. Production and Operations Management, 32(10), 3328-3346. doi: 10.1111/poms.14039.

[6] Bilenko, P., Eremenko, G., Zhukov, S., & Peregudov, V. (2023). Prospects and factors of the use of unmanned robotic vehicles in open-pit mining in Ukraine. Journal of Kryvyi Rih National University, 21(1), 126-132. doi: 10.31721/2306-5451-2023-1-56-126-133.

[7] Chen, S., Liu, J., Chen, B., & Suo, X. (2022). Universal fillet weld joint recognition and positioning for robot welding using structured light. Robotics and Computer-Integrated Manufacturing, 74, article number 102279. doi: 10.1016/j.rcim.2021.102279.

[8] Epping, K., & Zhang, H. (2018). A sustainable decision-making framework for transitioning to robotic welding for small and medium manufacturers. Sustainability, 10(10), article number 3651. doi: 10.3390/su10103651.

[9] Fanuc Corporation. (n.d.). Integrated reports. Retrieved from https://www.fanuc.co.jp/en/ir/annualreport/.

[10] Hnatenko, M. (2024). Elimination of casting defects in parts by microplasma cladding methods. Mining Journal of Kryvyi Rih National University, 22(1), 126-130. doi: 10.31721/2306-5435-2024-1-112-126-130.

[11] Javaid, M., Haleem, A., Singh, R.P., Suman, R., & Rab, S. (2021). Role of additive manufacturing applications towards environmental sustainability. Advanced Industrial and Engineering Polymer Research, 4(4), 312-322. doi: 10.1016/j.aiepr.2021.07.005.

[12] 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.

[13] Kairat, K., Ildar, P., Karygash, A., Saule, B., Indira, K., Markhaba, K., & Aizhan, B. (2023). Digital twins technology in the educational process of the aviation equipment repair. Indonesian Journal of Electrical Engineering and Computer Science, 32(2), 752-762. doi: 10.11591/ijeecs.v32.i2.pp752-762.

[14] Kawasaki Robotics. (n.d.). Kawasaki report (Annual & CSR Report). Retrieved from https://global.kawasaki.com/en/corp/ir/library/annual_report.html.

[15] Kharkiv Tractor Plant. (n.d.). Information for shareholders and stakeholders. Retrieved from https://xtz.ua/ua/public-information.html.

[16] Kołodziejczak, P., Bober, M., & Chmielewski, T. (2022). Wear resistance comparison research of high-alloy protective coatings for power industry prepared by means of CMT cladding. Applied Sciences, 12(9), article number 4568. doi: 10.3390/app12094568.

[17] Korzhyk, V.N., Kulak, L.D., Shevchenko, V.E., Kvasnitskiy, V.V., Kuzmenko, N.N., Liu, X., Cai, Y.X., Wang, L., Xie, H.W., & Zou, L.M. (2017). New equipment for production of super hard spherical tungsten carbide and other high-melting compounds using the method of plasma atomization of rotating billet. Materials Science Forum, 898 MSF, 1485-1497. doi: 10.4028/www.scientific.net/MSF.898.1485.

[18] Lee, D., & Han, K. (2024). Vision-based construction robot for real-time automated welding with human-robot interaction. Automation in Construction, 168, article number 105782. doi: 10.1016/j.autcon.2024.105782.

[19] Liashok, V., & Tykhanskyi, M. (2024). Intelligent tools in grinding processes as a powerful automation tool. Mining Journal of Kryvyi Rih National University, 22(1), 23-28. doi: 10.31721/2306-5435-2024-1-112-23-29.

[20] 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.

[21] 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.

[22] Motor Sich. (n.d.). Reporting. Retrieved from https://motorsich.com/ukr/investors/reg_Inf/richnataprom_inform/.

[23] Murugan, S.S., & Sathiya, P. (2024). Analysis of welding hazards from an occupational safety perspective. Vietnam Journal of Science, Technology and Engineering, 66(3), 63-74. doi: 10.31276/VJSTE.2023.0007.

[24] Ocansey, E.D., Hinterbichler, H., Holom, R.M., & Araz, M. (2024). Insights on weld quality using unsupervised learning: Clustering of MIG/MAG process signals. IOP Conference Series: Materials Science and Engineering, 1315, article number 012003. doi: 10.1088/1757-899X/1315/1/012003.

[25] Okumus, D., Fariya, S., Tamer, S., Gunbeyaz, S.A., Yildiz, G., Kurt, R.E., & Barlas, B. (2023). The impact of fatigue on shipyard welding workers’ occupational health and safety and performance. Ocean Engineering, 285, article number 115296. doi: 10.1016/j.oceaneng.2023.115296.

[26] Pires, J.N., Azar, A.S., Nogueira, F., Zhu, C.Y., Branco, R., & Tankova, T. (2022). The role of robotics in additive manufacturing: Review of the AM processes and introduction of an intelligent system. Industrial Robot, 49(2), 311-331. doi: 10.1108/IR-06-2021-0110.

[27] Pop, G., Pusca, A., Tucan, P., Moldovan, A., Jucan, D., & Gherman, B. (2024). Welding process automation using a robotic cell. Case study. In J. Machado, F. Soares, J. Trojanowska, E. Ottaviano, P. Valášek, M. Reddy, E.A. Perondi & Ye. Basova (Eds.), Innovations in mechanical engineering III (pp. 384-393). Cham: Springer. doi: 10.1007/978-3-031-62684-5_34.

[28] Proia, S., Carli, R., Cavone, G., & Dotoli, M. (2021). Control techniques for safe, ergonomic, and efficient human-robot collaboration in the digital industry: A survey. IEEE Transactions on Automation Science and Engineering, 19(3), 1798-1819. doi: 10.1109/TASE.2021.3131011.

[29] Puzyrov, O., & Hrechka, A. (2024). Analysis of the effectiveness of implementing the drawing state for the preparation of metalworking blanks for machines with CNC. Technologies and Engineering, 25(5), 71-78. doi: 10.30857/2786-5371.2024.5.7.

[30] Rocha, F., et al. (2021). ROSI: A robotic system for harsh outdoor industrial inspection-system design and applications. Journal of Intelligent & Robotic Systems, 103, article number 30. doi: 10.1007/s10846-021-01459-2.

[31] Sarivan, I.M., Madsen, O., & Wæhrens, B.V. (2024). Automatic welding-robot programming based on product-process-resource models. International Journal of Advanced Manufacturing Technology, 132(3), 1931-1950. doi: 10.1007/s00170-024-13409-x.

[32] Sharma, A., Chaturvedi, R., Singh, P.K., & Sharma, K. (2021). AristoTM robot welding performance and analysis of mechanical and microstructural characteristics of the weld. Materials Today: Proceedings, 43, 614-622. doi: 10.1016/j.matpr.2020.12.158.

[33] Srinivasulu, R., Rao, Y.S., Vempati, S., & Haribabu, U. (2022). Designing arc welding application using Yaskawa robot. International Journal of Mechanical Engineering, 7(2), 2493-2510.

[34] Sun, G., Wang, Z., Lu, Y., Chen, M., Yang, K., & Ni, Z. (2022). Underwater laser welding/cladding for high-performance repair of marine metal materials: A review. Chinese Journal of Mechanical Engineering, 35(1), article number 5. doi: 10.1186/s10033-021-00674-0.

[35] Vasilev, M., MacLeod, C.N., Loukas, C., Javadi, Y., Vithanage, R.K., Lines, D., Mohseni, E., Pierce, S.G., & Gachagan, A. (2021). Sensor-enabled multi-robot system for automated welding and in-process ultrasonic NDE. Sensors, 21(15), article number 5077. doi: 10.3390/s21155077.

[36] Wang, X., Zhou, X., Xia, Z., & Gu, X. (2021). A survey of welding robot intelligent path optimization. Journal of Manufacturing Processes, 63, 14-23. doi: 10.1016/j.jmapro.2020.04.085.

[37] Yang, S., Zhong, Y., Feng, D., Li, R.Y., Shao, X.F., & Liu, W. (2022). Robot application and occupational injuries: Are robots necessarily safer? Safety Science, 147, article number 105623. doi: 10.1016/j.ssci.2021.105623.

[38] Zhang, J., Shao, G., Fan, J., Wang, L., & Zhang, D. (2022). A review on parallel development of flux design and thermodynamics subject to submerged arc welding. Processes, 10(11), article number 2305. doi: 10.3390/pr10112305.

[39] Zhang, P., Wang, J., Zhang, F., Xu, P., Li, L., & Li, B. (2022). Design and analysis of welding inspection robot. Scientific Reports, 12(1), article number 22651. doi: 10.1038/s41598-022-27209-4.

[40] Zhao, X., Wu, C., & Liu, D. (2021). Comparative analysis of the life-cycle cost of robot substitution: A case of automobile welding production in China. Symmetry, 13(2), article number 226. doi: 10.3390/sym13020226.

[41] Zheng, C., An, Y., Wang, Z., Wu, H., Qin, X., Eynard, B., & Zhang, Y. (2022). Hybrid offline programming method for robotic welding systems. Robotics and Computer-Integrated Manufacturing, 73, article number 102238. doi: 10.1016/j.rcim.2021.102238.