Online weld pool contour extraction and seam width prediction based on mixing spectral vision

Abstract

In this paper, based on gas–metal–arc welding (GMAW), we used a passive vision sensing system and proposed a double-path imaging method to capture weld pool images, and empirically and theoretically demonstrated the optimal bands. According to the mixed spectra of self-emitted radiation of the weld pool and the arc spectra, we selected 660-nm narrowband and 850-nm long-pass as the system’s working bands. Two cameras with 660-nm narrowband filter and 850-nm long-pass filter were used to capture weld pool images at the background level through a synchronous acquisition equation and weld pool images with high signal-to-noise ratio were obtained. After image registration, we used Gradient and Gray-based Neighbor Superpixel Merging (GNSM) method to extract the contour of weld pool image. Comparing with other algorithms, the proposed algorithm can obtain an effective and accurate contour of the weld pool image. Then we proposed an online seam width prediction method before seam formation which is based on the contour of the weld pool image. We used Gaussian distribution to fit the pixel width of the contour and the corresponding seam width measured by three-dimensional reconstruction. By comparatively analyzing the fitting deviation and the actual measurement results, we concluded that the deviation of weld seam width prediction was within 0.20 mm.