In order to build a better understanding of the relationship between depositing mode and porosity, microstructure, and properties in wire + arc additive manufacturing (WAAM) 2319-Al components, several Al-6.3%Cu deposits were produced by WAAM technique with cold metal transfer (CMT) variants, pulsed CMT (CMT-P) and advanced CMT (CMT-ADV). Thin walls and blocks were selected as the depositing paths to make WAAM samples. Porosity, microstructure and micro hardness of these WAAM samples were investigated. Compared with CMT-P and thin wall mode, CMT-ADV and block process can effectively reduce the pores in WAAM aluminum alloy. The microstructure varied with different depositing paths and CMT variants. The micro hardness value of thin wall samples was around 75 HV from the bottom to the middle, and gradually decreased toward the top. Meanwhile, the micro hardness value ranged around 72–77 HV, and varied periodically in block samples. The variation in micro hardness is consistent with standard microstructure characteristics.

Aluminum–Lithium (Al–Li) alloy is a topic of great interest owing to its high strength and light weight, but there are only a few applications of Al–Li alloy in wire + arc additive manufacturing (WAAM) process. To identify its feasibility in WAAM process, a special AA2050 Al–Li alloy wire was produced and employed in the production of straight-walled components, using a WAAM system based on variable polarity gas tungsten arc welding (VP-GTAW) process. The influence of post-deposited heat treatment on the microstructure and property of the deposit was investigated using optical micrographs (OM), scanning electron microscopy (SEM), X-ray diffraction (XRD), hardness and tensile properties tests. Results revealed that the microstructures of AA2050 aluminum deposits varied with their location layers. The upper layers consisted of fine equiaxed grains, while the bottom layer exhibited a coarse columnar structure. Mechanical properties witnessed a significant improvement after post-deposited heat treatment, with the average micro-hardness reaching 141HV and the ultimate tensile strength exceeding 400 MPa. Fracture morphology exhibited a typical ductile fracture.

Tensile properties optimization of AA 5456 aluminum alloy was carried out with hybrid ultrasonic frequency pulsed variable polarity gas tungsten arc wending (HPVP-GTAW). An orthogonal method was employed to conduct the experiments, and the tensile properties of AA 5456 aluminum alloy welded joints were measured and analyzed. Regression models were developed based on the least square estimation by taking tensile strength, yield strength, percent elongation, and ratio of reduction in area as response functions of variable polarity frequency f_L, pulse frequency f_H and a dimensionless parameter ψ, which were calculated by background current I_b, peak current I_p, and pulse duration δ, respectively. The developed regression equations were checked for validity by coefficient of correlation r² and confirmatory experiments. Optimum parameters of HPVP-GTAW were achieved through the discussion on response surfaces and contour plots drawn using the regression equations.

Wire arc additive manufacturing (WAAM) technology has achieved significant advancements in fabricating complex metal components, yet defects such as surface porosity, lack of fusion, and slag inclusion continue to compromise quality and efficiency. To address challenges in detecting welding slag and micro-pores, we introduce an enhanced You Only Look Once (YOLO)v8n-Attention-Refined Feature Module (ARFM) architecture integrating receptive field attention convolution, an attention-based feature pyramid network, and the Focaler-minimum point distance intersection over union metric. This integration markedly improves the precision and resilience of defect recognition. Moreover, we assessed the defect detection capabilities of YOLOv3t, YOLOv5n, YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n in WAAM and developed a composite defect dataset incorporating aluminum, titanium, and stainless steel specimens. The experiments reveal that YOLOv8n achieved the highest overall detection effectiveness (mean average precision [mAP]@0.5 = 0.921). YOLOv10n exhibited marginal superiority in slag inclusion detection (mAP@0.5 = 0.886) with a peak throughput of 54.35 frames per second (FPS), while YOLOv5n converged the fastest within 500 epochs. Following the introduction of ARFM, mAP@0.5 increased to 0.944 (+2.4%), and slag inclusion detection reached 0.929 (+7.3%). However, the frame rate declined to 38 FPS, which suffices for basic real-time monitoring but remains inadequate for high real-time scenarios. This study provides an important reference for deploying YOLO-series models for real-time monitoring of WAAM and closed-loop control systems.