Monitoring of Welding Processes with Application of Artificial Neural Networks

Євгенія Петрівна Чвертко, Андрій Євгенович Пірумов, Микола Віталійович Шевченко

Abstract


The paper presents a summary of methods of monitoring systems’ development for the processes involving heating of filler material and/ or base metal by the electric current and with periodical shortages of the welding circuit. The processes investigated were MAG welding, underwater flux-cored welding and flash-butt welding. Details of experiments, primary data processing procedures based on statistical analysis methods are described, the aim of primary processing being obtaining of informative parameters of the welding processes. Details of neural network structure development, training and control sequences generation, nets training and adequacy check are presented as well. Comparison of determination of process variations (edge shifting, electrode outlet, variation of gap, variation of joint line – for arc welding methods; decreasing of open-circuit voltage and specimen travel speed – for flash-butt welding) is presented. The on-line monitoring systems based on neural networks developed for evaluation of process deviations are also believed to be adequate for determination of process violations resulting in disturbances of welding parameter and can be used for prediction of possible defects in the welded joints.

Keywords


MAG welding; Underwater FCAW welding; Flash-butt welding; Process monitoring; Detection of defects

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References


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DOI: https://doi.org/10.20535/1810-0546.2014.2.54874

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