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Vision sensing of P-GMAW weld pool using narrow band filters with different center wavelengths

China welding 2013年第1期22卷 41-46页

作者：梁志敏汪殿龙王军 School of Material Science and Technology Hebei University of Science and Technology Shijiazhuang 050018

The selection of narrow band filter＇s center wavelength has an important influence on the quality of the weld pool image captured in passive vision mettmd for controlling arc welding process. In this paper, 29 narrow band filters with different center wavelengths ranging from ultraviolet to near infrared wavelength were used to capture images of weld pool during both the peak current period and the base current period of P-GMA W （ pulsed gas metal arc welding）. The experimental results showed that the filters with near infrared center wavelength could weaken the arc most during the peak current period, while during the base current period the entire weld pool could be seen with the filters with wavelength above 600 nm. The camera spectral response and the radio of arc to weld pool emission were considered to analyze the phenomenon of experimental results.

关键词： narrow band filter weld pool vision sensing pulsed gas metal arc welding

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Neural network modeling for weld shape process of P-GMAW

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China welding 2007年第1期16卷 68-71页

作者：闫志鸿吴林张广军高洪明 State Key Laboratory of Advanced welding Production Technology Harbin Institute of Technology Harbin 150001

Weld shape control is a fundamental issue in automatic welding. In this paper, a double side visual system is established for pulsed gas metal arc welding （ P-GMAW） , and both topside and backside weld pool images can be captured and stored continuously in real time. By analyzing the weld shape regulation with the molten metal volume, some topside weld pool characterized parameters （WPCPs） are proposed for determining penetration in butt welding of thin mild steel. Moreover, some BP network models are established to predict backside Weld pool width with welding parameters and WPCPs as inputs.

关键词： pulsed gas metal arc welding weld shape neural network modeling

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