Binocular Vision-Based Image Extraction and Feature Analysis of Weld Beads in 316L Wire Arc Additive Manufacturing.

To address the challenges of low image quality and difficult feature extraction of weld beads caused by the complex dynamics of the molten pool, intense arc light, and spatter interference during wire arc additive manufacturing (WAAM) of 316L stainless steel, this paper develops a binocular vision-based dynamic molten pool tracking system and conducts image processing and feature analysis. Two high-speed CMOS cameras are employed to capture images of the molten pool and weld bead. Camera calibration is performed to convert pixel coordinates to world coordinates. The denoising performance of five filtering methods, namely mean, Gaussian, median, maximum, and minimum filters, is systematically compared, and the minimum filter is selected for noise reduction. Adaptive threshold binarization, adapthisteq image enhancement, and morphological threshold segmentation are integrated to effectively separate the weld bead from the background. Four edge detection algorithms-Sobel, Robert, Laplacian, and Canny-are compared, and the Canny algorithm combined with Hough transform line fitting is determined to achieve complete and continuous extraction of the weld bead contour. The Intersection over Union (IoU) metric is introduced for image quality screening. When IoU is set to 0.3, the detection accuracy exceeds 90%, effectively eliminating defective images caused by spatter, explosion, trailing, and other disturbances. The proposed method facilitates stable extraction of geometric parameters (e.g., pixel area of the weld bead and height/width of the molten pool), thereby offering a feasible image-processing solution for dynamic molten-pool monitoring and online quality assessment of 316L stainless steel components fabricated by wire arc additive manufacturing.