The modeling of welding pool surface reflectance of aluminum alloy pulse GTAW

The information of welding pool surface height plays an important role in the precise control of weld formation. The height measurement of welding pool surface is very difficult because there exists splash, fume, high temperature, electromagnetic disturbance, and so on during welding and welding pool have the characteristics such as small volume, short existence time in high temperature, high temperature, the flow in welding pool and concurrence of metal melting and freezing, etc. The application of aluminum alloy is wide because of low melting point, large conductivity of electric and heat, small density and so on. Welding is an important manufacture technology in aluminum alloy application where the quality control is too important. The color of aluminum alloy does not obviously change after melting which cause the difficulty to estimate welding estate. Visual sensing is a promising method to acquire the shape information of welding pool. The theory of surface height calculation of pulse GTAW welding pool based on SFS is to calculate the surface height from welding pool image whose key is to build up the surface reflectance model of welding pool. Based on the imaging characteristics of aluminum alloy pulse GTAW welding pool, the surface reflectance model is built after analyzing arc intensity, filter system, welding pool shape and reflectance characteristics. With smooth constraint condition of the welding pool surface and variable factor SOR method, the height of welding pool surface is calculated and error analyzed.

[1]  C.-C. Jay Kuo,et al.  Shape from Shading with a Linear Triangular Element Surface Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  L. B. Wolff.  Diffuse-reflectance model for smooth dielectric surfaces , 1994 .

[3]  Demetri Terzopoulos,et al.  The Computation of Visible-Surface Representations , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Bui Tuong Phong.  Illumination for computer generated pictures , 1975, Commun. ACM.

[5]  Jianjun Wang.  IMAGE TAKING AND PROCESSING OF MOLTEN POOL CHARACTERS DURING ALUMINUM ALLOY TIG WELDING , 2003 .

[6]  Stanley Y. W. Su,et al.  Web Information Systems – WISE 2004 , 2004, Lecture Notes in Computer Science.

[7]  Shree K. Nayar,et al.  Generalization of the Lambertian model and implications for machine vision , 1995, International Journal of Computer Vision.

[8]  Takeo Kanade,et al.  Surface Reflection: Physical and Geometrical Perspectives , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Ping-Sing Tsai,et al.  Shape from Shading: A Survey , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Michael J. Brooks,et al.  The variational approach to shape from shading , 1986, Comput. Vis. Graph. Image Process..

[11]  Thomas O. Binford,et al.  Local shape from specularity , 1988, Comput. Vis. Graph. Image Process..

[12]  Takeo Kanade,et al.  Shape from interreflections , 2004, International Journal of Computer Vision.