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Abstract <p>In this paper, we consider current challenges in improving the quality of welded structures at enterprises of different industries. It seems necessary to organize effective training of welders, as well as the distribution of work among them to achieve high-quality welds. Traditional and innovative approaches to welding quality control are discussed, including the use of information and measurement systems involving artificial intelligence technologies. We propose solutions based on adaptive swarm intelligence algorithms and evolutionary modeling that are aimed at improving the efficiency of welding production control owing to optimization of training and task distribution among welders. The results of implementing the developed systems in production practice are highlighted that confirm a reduction in weld defects and an improvement in the quality of specialist training.</p>

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Intelligent Control of the Information and Measurement System in Welding Production

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • E. V. Zarauchatskaya,
  • A. E. Misnik

摘要

Abstract

In this paper, we consider current challenges in improving the quality of welded structures at enterprises of different industries. It seems necessary to organize effective training of welders, as well as the distribution of work among them to achieve high-quality welds. Traditional and innovative approaches to welding quality control are discussed, including the use of information and measurement systems involving artificial intelligence technologies. We propose solutions based on adaptive swarm intelligence algorithms and evolutionary modeling that are aimed at improving the efficiency of welding production control owing to optimization of training and task distribution among welders. The results of implementing the developed systems in production practice are highlighted that confirm a reduction in weld defects and an improvement in the quality of specialist training.