Technical Diagnostics and Non-Destructive Testing, 2026, №01

2026 №01 (01)

DOI of Article 10.37434/tdnk2026.01.02

2026 №01 (03)

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"Tekhnichna Diahnostyka ta Neruinivnyi Kontrol" (Technical Diagnostics and Non-Destructive Testing) #1, 2026, pp. 8-22

The application of artificial intelligence in welding and related technologies

L.M. Lobanov, O.V. Makhnenko, O.S. Milenin, M.G. Malhin, G.Yu. Saprykina, O.M. Savitskaya

E.O. Paton Electric Welding Institute of the NAS of Ukraine 11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine. E-mail: makhnenko@paton.kiev.ua

The use of Artifi cial Intelligence (AI) systems based on Large Language Models off ers signifi cant opportunities for welding specialists to analyze vast amounts of information available online when preparing scientifi c articles and reports, as well as for solving standard tasks in mathematics, physics, chemistry, etc. But the implementation of specialized AI models in welding is highly advisable. These models can eff ectively address challenges such as optimizing welding parameters, analyzing weld quality using computer vision methods, automating welding for repetitive tasks, monitoring the condition of critical welded structures, creating digital twin systems, and in the fi eld of welder training. The utilization of AI systems in welding and related technologies can provide substantial advantages in the development of new welded products and welding techniques through optimized processes. 46 Ref., 1 Tabl., 11 Fig.
Keywords: welding, artifi cial intelligence, neural networks, welding parameter optimization, weld quality control, robotic welding, monitoring systems, welder training

Received: 07.08.25
Received in revised form: 28.10.25
Accepted: 10.04.26
Posted online: 23.04.2026

References

1. Welding Automation and AI are Changing the Industry – Featuring: NovEyeTM Autonomy. Posted: January 24, 2025. https://www.novarctech.com/resources/blog/welding/welding-automation-and-ai-are-changing-the-industry-featuring-noveye-autonomy/
2. O’Regan, G. (2021) A brief history of computing. Springer.
3. (2020) A guided tour of artificial intelligence research. Vol. I: Knowledge representation, reasoning and learning. Ed. by P. Marquis, O. Papini, H. Prade. Switzerland AG 202: Springer Nature.
4. (2024) A short history of AI. The Economist. https://www.economist.com/schools-brief/2024/07/16/a-short-history-of-ai
5. Russell, S., Norvig, P. (2016) Artificial intelligence: A modern approach. Moscow, St. Petersburg, Kyiv, Williams [in Russian].
6. Crevier, D. (1993) The Tumultuous History of the Search for Artificial Intelligence.
7. Glushkov, V.M. (1962) Synthesis of digital automata. Moscow, Fizmatgiz [in Russian].
8. Ivakhnenko, A.G., Lapa, V.G. (1965) Cybernetic predictive devices. Kyiv, Naukova Dumka [in Russian].
9. Amosov, M.M. (1965) Modeling of thinking and psyche. Kyiv, Naukova Dumka [in Russian].
10. Glushkov, V.M. (1966). Thinking and cybernetics. Moscow, Znanie [in Russian].
11. Ivakhnenko, O.G., Zaichenko, Y.P. (1967) Machines begin to think. Kyiv, Tovarystvo «Znannya» [in Ukrainian].
12. Amosov, M.M. (1969) Artificial intelligence. Kyiv, Naukova Dumka [in Russian].
13. Amosov, N.M. (1969) Modeling of Thinking and the Mind. New York, Springer.
14. Waterman, D. (1989) A guide to expert systems. Moscow, Mir [in Russian].
15. Makhnenko, V.I., Skośnyagin, Yu.A., Lavrinets, I.G., Saprykina, G.Yu. (1991) Expert systems in welding. Kyiv, PWI [in Russian].
16. Saprykina, G.Yu. (1995). Development of the expert system on Design of Technology for Submerged Arc Welding of Steels. In: Synopsis of Thesis for Dis. ... Cand. of Techn. Sci. Degree. Kyiv [in Russian].
17. Leith, P. (2010) The rise and fall of the legal expert system. European J. of Law and Technology, 1(l), 94–106. DOI: https://doi.org/10.1080/13600869.2016.1232465
18. Goodfellow, I., Bengio, Y., Courville, A. (2016) Deep Learning. The MIT Press.
19. Turp, E. (2023) The potential and danger of artificial intelligence. https://meetings.imf.org/ru/IMF/Home/ Publications/fandd/issues/2023/12/B2B-Artificial- Intelligence-promise-peril-Tourpe [in Russian].

20. Baicun Wanga, Hub, S.J., Lei Suna, Freihe, T. (2020) Intelligent welding system technologies: State-of-the-art review and perspectives. J. of Manufacturing Systems, 56, 374–391. DOI: https://doi.org/10.1016/j.jmsy.2020.06.020
21. Berkay Eren, Mehmet Ali Guvenc, Selcuk Mistikoglu (2020) Artificial intelligence applications for friction stir welding: A review. Metals and Materials Intern., 27(6), 193–219. DOI: https://doi.org/10.1007/s12540-020-00854-y
22. Haykin, S. (2009) Neural Networks and Learning Machines. Third Edition. Prentice Hall, New York.
23. Valizadeh, M., Wolff, S.J. (2022) Convolutional neural network applications in additive manufacturing: A review. Advances in Industrial and Manufacturing Engineering, 4, 100072. DOI: https://doi.org/10.1016/j.aime.2022.100072
24. Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li (2019) Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering, 5(4), 721–729. DOI: https://doi.org/10.1016/j.eng.2019.04.012
25. Build a Retrieval Augmented Generation (RAG) (Pts 1, 2) https://python.langchain.com/docs/tutorials/rag/
26. Keith Bourne (2024) Unlocking Data with Generative AI and RAG. – Packt Publishing.
27. Okuyucu, H., Kurt, A., Arcaklioglu, E. (2007) Artificial neural network application on the friction stir welding of aluminum plates. Materials and Design, 28(1), 78–84. DOI: https://doi.org/10.1016/j.matdes.2005.06.003
28. D’Orazio, A., Forcellese, A., Simoncini, M. (2018) Prediction of the vertical force during FSW of AZ31 magnesium alloy sheets using an artificial neural network-based model. Neural Computing and Application, 31, 7211–7226. DOI: https://doi.org/10.1007/s00521-018-3562-6)
29. Nadeau, F., Thériault, B., Gagné, M.-O. (2020) Machine learning models applied to friction stir welding defect index using multiple joint configurations and alloys. Materials: Design and Applications, 234(5), 752–765. DOI: https://doi.org/10.1177/1464420720917415.
30. Gyasia, E.A., Handroosa, H., Kah, P. (2019) Survey on artificial intelligence (AI) applied in welding: A future scenario of the influence of AI on technological, economic, educational and social changes. Procedia Manufacturing, 38, 702–714. DOI: https://doi.org/10.1016/j.promfg.2020.01.095
31. Aldalur, E., Suarez, A., Curiel, D., Viega, F., Vilanueva, P. (2023) Intelligent and adaptive system for welding process automation in T-shaped joints. Metals, 13(9), 1532. DOI: https://doi.org/10.3390/met13091532
32. Eren, B., Demir, M.H., Mistikoglu, S. (2023) Welding robot design with machine learning based intelligent vision system. Intelligent methods in engineering sciences, 2(2) 048–051. DOI: https://doi.org/10.58190/imiens.2023.12
33. Ucar, A., Karakose, M., Kirimça, N. (2024) Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends. Applied Science.
34. Murtaza, A.A., Saher, A., Zafar, M.H., Moosavi, S.K.R., Muhammad Faisal Aftab, M.F., Sanfilippo, F. (2024) Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering, 24, 102935. DOI: https://doi.org/10.1016/j.rineng.2024.102935
35. Huang, C., Bu, S., Lee, H.H., Chan, C.H., Kong, S.W., Yung, W.K.C. (2024) Prognostics and health management for predictive maintenance: A review. J. of Manufacturing Systems, 75, 78–101. DOI: https://doi.org/10.1016/j.jmsy.2024.05.021
36. Mallioris, P., Aivazidou, E., Bechtsis, D. (2024) Predictive maintenance in Industry 4.0: A systematic multi-sector mapping. CIRP J. of Manufacturing Science and Technology, 50, 80–103. DOI: https://doi.org/10.1016/j.cirpj.2024.02.003
37. Liu, H., Qi, J., Feng, H., Fan, M. (2025) Research on state monitoring and diagnosis models for multi-state systems based on Petri nets. Results in Engineering, 27, 106249. DOI: https://doi.org/10.1016/j.rineng.2025.106249
38. Thirumalaiselvi, A., Sasmal, S. (2024) Machine learningbased acoustic emission technique for corrosion-induced damage monitoring in reinforced concrete structures. Engineering Applications of Artificial Intelligence, 137, Part A, 109121. DOI: https://doi.org/10.1016/j.engappai.2024.109121
39. Zhang, H., Yan, J., Yang, J., Meng, W., Chen, S. (2025) Two-stage point cloud registration using multi-scale edge convolution for digital twin-based bridge construction progress monitoring. Automation in Construction, 178, 106415. DOI: https://doi.org/10.1016/j.autcon.2025.106415
40. Jiao, W.W., Zhao, D., Mei, X., Yang, S., Zhang, X., Li, L., Xiong, J. (2024) Digital twin for weld pool evolution by data-physics integrated driving. J. of Manufacturing Processes, 131, 947–957. DOI: https://doi.org/10.1016/j.jmapro.2024.09.022
41. Siyuan Chen, S., Turanoglu Bekar, E., Bokrantz, J., Skoogh, A. (2025) AI-enhanced digital twins in maintenance: Systematic review, industrial challenges, and bridging research–practice gaps. J. of Manufacturing Systems, 82, 678–699. DOI: https://doi.org/10.1016/j.jmsy.2025.07.006
42. Zhang, J., Li, C., Deng, C., Luo, T., Deng, R., Luo, D., Tao, G., Cao, H. (2025) Toward digital twins for intelligence manufacturing: Self-adaptive control in assisted equipment through multi-sensor fusion smart tool real-time machine condition monitoring. J. of Manufacturing Systems, 82, 301–318. DOI: https://doi.org/10.1007/s42452-024-06206-4
43. Ye, X.-W., Sun, Z., Lu, J. (2023) Prediction and early warning of wind-induced girder and tower vibration in cable-stayed bridges with machine learning-based approach. Engineering Structures, 275, Part A, 115261. DOI: https:// doi.org/10.1016/j.engstruct.2022.115261
44. Abebe Derseh, S., Alemu Mohammed, T. (2023) Bridge structures under progressive collapse: A comprehensive state-of-the-art review. Results in Engineering, 18, 101090. DOI: https://doi.org/10.1016/j.rineng.2023.101090
45. Sunjoong Kim, Sun-Ho Lee, Sejin Kim (2023) Pointwise multiclass vibration classifi cation for cable-supported bridges using a signal-segmentation deep network. Engineering Structures, 279, 115599. DOI: https://doi.org/10.1016/j. engstruct.2023.115599
46. Juihuei Yao, Peleshenko, S.I., Korzhik, V.N., Khaskin ,V.Yu., Kvasnitsky, V.V. (2017) Concept of creation of an improved artifi cal intelligence system and computerized trainer for virtual welding. The Paton Welding J., 5–6, 19–26. DOI: https://doi.org/10.15407/tpwj2017.06.04

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Suggested Citation

L.M. Lobanov, O.V. Makhnenko, O.S. Milenin, M.G. Malhin, G.Yu. Saprykina, O.M. Savitskaya (2026) The application of artificial intelligence in welding and related technologies. Technical Diagnostics and Non-Destructive Testing, 01, 8-22. https://doi.org/10.37434/tdnk2026.01.02

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