Abstract
A narrow-seam identification algorithm is developed to achieve seam tracking in keyhole deep-penetration tungsten inert gas welding (TIG). The welding images are captured by a high-dynamic-range camera and denoised by a bilateral filter based on a noise model analysis. The arc area is extracted as a fixed region of interest. Then, an improved Otsu algorithm and a parabolic fitting algorithm are used to obtain the centerline of the arc. The seam area is extracted as an adaptive region of interest based on a proposed HOG+LBP algorithm. Thereafter, a continuous single-pixel edge contour is extracted by the canny algorithm, and a proposed contour curvature evaluation method is used to obtain the corresponding pixel coordinates. After testing and analysis, the deviation can be reliably detected with an average measurement error within ± 0.04 mm. As a result, the algorithm proposed in this study can accurately identify the deviation during keyhole deep-penetration TIG welding, and has application prospects in the narrow-seam welding field.
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