Abstract
This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.
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Chen, S.B., Zhang, Y., Qiu, T. et al. Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process. Journal of Intelligent and Robotic Systems 36, 191–208 (2003). https://doi.org/10.1023/A:1022652706683
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DOI: https://doi.org/10.1023/A:1022652706683