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

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This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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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