AI-based welding process monitoring for quality control in large-diameter pipe manufacturing
Proceedings Publication Date
Dr. Sergej Gook
Sergej Gook, Bassel El-Sari, Max Biegler, Michael Rethmeier
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The paper presents the experimental results of the development of a multi-channel system for monitoring and quality assurance of the multi-wire submerged arc welding (SAW) process for the manufacture of large-diameter pipes. Process signals such as welding current, arc voltage and the acoustic signal emitted from the weld zone are recorded and processed to provide information on the stability of the welding process. Experiments have shown that the acoustic pattern of the SAW process contains the most diagnostic information in the frequency range between 30 Hz and 2 kHz. In the spectrogram of the acoustic signal, which represents the time course of the frequency spectrum of the welding process, the formation of welding irregularities such as undercuts could be reliably identified. The on-line quality assessment of the produced weld is carried out in combination with methods of artificial intelligence (AI). From the results obtained, it can be concluded that the use of the latest concepts in welding and automation technology, combined with the high potential of AI, can achieve a new level of quality assurance in pipe manufacturing.

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