Qiang Zhu

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.

Wire arc additive manufacturing (WAAM) has emerged as a promising technology for producing large-scale metal components due to its high deposition efficiency, low material cost, and design flexibility. However, the widespread industrial adoption of WAAM is hindered by challenges in geometric accuracy, process stability, and defect control, which are closely related to two critical aspects: trajectory planning and real-time weld seam tracking. This review provides a comprehensive and critical analysis of recent advances in both fields, with an emphasis on their interconnection rather than treating them as separate research streams. Unlike existing reviews that primarily summarize path planning algorithms or image processing techniques in isolation, this paper explicitly examines the integration challenges and synergistic potential between offline trajectory optimization and online vision-based monitoring. Key topics include adaptive path strategies for sharp corners and intersections, interlayer filling methods to mitigate heat accumulation and residual stress, as well as passive and active visual sensing technologies for molten pool characterization and defect detection. The review further identifies a persistent gap in closed-loop systems that combine real-time image feedback with dynamic path replanning. Based on the analysis of representative studies, current limitations are discussed and future research directions are proposed, including the development of digital twins, multi-modal data fusion, and reinforcement learning-based adaptive control. This review offers a distinct perspective aimed at advancing intelligent, high-precision WAAM systems for complex metal components.

Continuous innovations in welding and additive manufacturing (AM) technologies have introduced challenges related to process stability and uncertainties in ensuring high quality. Given the high complexity and transient nature of these processes, effective online acoustic monitoring is crucial for ensuring manufacturing quality and improving processing efficiency. This paper first elucidates the principles of acoustic signal generation in welding and additive manufacturing. It then provides a comprehensive review of the application of acoustic methods for quality monitoring in these processes, covering both structural acoustic emission (AE) and airborne acoustic monitoring techniques. Finally, it summarizes the current challenges and issues faced by acoustic monitoring technologies in welding and additive manufacturing and outlines potential future development directions.

Wire arc additive manufacturing (WAAM) technology enables the fabrication of functionally graded materials (FGMs) by adjusting the wire feed speed of different welding wires in a layer-by-layer manner. This study aimed to produce SS 316L/Inconel 625 FGMs with varying transition compositions using dual-wire arc additive manufacturing (D-WAAM). An optimization strategy for transition gradients was implemented to exclude component regions that are prone to defect formation (notably cracking), as well as to retain other component regions, thereby enhancing the overall mechanical properties of FGMs. The study revealed grain boundary cracking and demonstrated the lowest microhardness and tensile properties within a 20 wt.% Inconel 625 transition gradient zone, which negatively impacts the overall mechanical properties of FGMs. Then, as the content of Inconel 625 in the first transition region increased, cracks disappeared, microhardness increased and better tensile properties were obtained. The most optimal mechanical properties were enriched at 50 wt.% Inconel 625 content. In conclusion, the compositional gradient optimization strategy proves efficacious in eliminating component regions with poor mechanical properties and microdefects, ensuring excellent overall mechanical characteristics of FGMs.

Aluminum–magnesium (Al–Mg) alloys, known for their lightweight properties, are extensively utilized and crucial in the advancement of wire and arc additive manufacturing (WAAM) for direct high-quality printing—a focal point in additive manufacturing research. This study employed 1.2 mm ER5356 welding wire as the raw material to fabricate two sets of 30-layer thin-walled structures. These sets were manufactured using two distinct welding modes, speed-twin pulse (STP) and twin pulse (TP). Comparative evaluations of the surface quality, microstructures, and mechanical properties of the two sets of samples indicated that both the STP and TP modes were suitable for the WAAM of Al–Mg alloys. Analyses of grain growth in the melt pools of both sample sets revealed a non-preferential grain orientation, with a mixed arrangement of equiaxed and columnar grains. The STP mode notably achieved a refined surface finish, a reduced grain size, and a slight increase in tensile strength compared to the TP mode. From the comparison of the tensile data at the bottom, middle, and top of the two groups of samples, the additive manufacturing process in the STP mode was more stable.

To solve the challenge of welding aluminum alloys, a unified adjustment model for Gaussian pulse welding is established. This model can achieve improved welding performance by adjusting the base current of the weak pulse group within a specific range of average welding current inputs. The flat overlaying welding is carried out on the base material: 6061 aluminum alloys with thicknesses of 2 mm, 3 mm, and 5 mm. A stable welding process, indicated by reduced spatter, is produced, with a soft arc sound and good repeatability in the waveforms of the real-time current and voltage. The weld has a shiny surface and regular fish scale ripples. Metallographic analysis shows that the fusion line is clear, and there are no visible defects, while the weld zone has fine dendritic structures. The tensile test results indicate that fractures occur in the heat-affected zone, and that the tensile strength reaches about 68% of that of the base metal.