The arc torch angle greatly affected the deposition characteristics in the wire arc additive manufacturing (WAAM) process, and the relation between the droplet transition behavior and macrostructure morphology was unclear. This work researched the effect of torch angle on the formation accuracy, droplet transition behavior and the mechanical properties in the WAAM process on a ZL205A aluminum alloy. The results suggested that at the obtuse torch angle, part of the energy input was used to heat the existing molten pool, which was optimized for the longer solidification period of the molten pool. Therefore, the greater layer penetration depth at 100° resulted in the improved layer-by-layer combination ability. The obtuse torch angle was associated with the better formation accuracy on the sidewall surface due to the smaller impact on the molten pool, which was influenced by both the arc pressure and droplet impact force. The eliminated pores were optimized for the mechanical properties of depositions at a torch angle of 100°; thus, the tensile strength and elongation attained maximum values of 258.6 MPa and 17.1%, respectively. These aspects made WAAM an attractive mode for manufacturing large structural components on ZL205A aluminum alloy.

The detection of keyhole-induced pore positions is a critical procedure for assessing laser welding quality. Considering the detection error due to pore migration and noise interference, this research proposes a regional prediction model based on the time–frequency-domain features of the laser plume. The original plume signal was separated into several signal segments to construct the morphological sequences. To suppress the mode mixing caused by environmental noise, variational modal decomposition (VMD) was utilized to process the signals. The time–frequency features extracted from the decomposed signals were acquired as the input of a backpropagation (BP) neural network to predict the pore locations. To reduce the prediction error caused by pore migration, the effect of the length of the signal segments on the prediction accuracy was investigated. The results show that the optimal signal segment length was 0.4 mm, with an accuracy of 97.77%. The 0.2 mm signal segments failed to eliminate the negative effects of pore migration. The signal segments over 0.4 mm resulted in prediction errors of small and dense pores. This work provides more guidance for optimizing the feature extraction of welding signals to improve the accuracy of welding defect identification.

The joining of solid-state sintered silicon carbide (SiC) was achieved using a novel Si-Ti-Al ternary alloy via vacuum brazing. This study investigates a predominantly non-carbide-dominated interfacial bonding mechanism and the influence of brazing temperature on joint microstructure, mechanical properties, and high temperature reliability. The high Si content promoted the incorporation of Ti into Ti–Si phases within the brazed seam, thereby limiting the amount of Ti available for reaction with SiC. No continuous TiC layer was detected within the spatial resolution of the employed characterization methods. Minor discrete Al₄C₃ precipitates were identified at the interface but did not constitute the dominant bonding phase. Brazing at 1360 °C yielded an optimal microstructure featuring highly regular coral-like eutectic clusters, resulting in a peak room temperature shear strength of 102.8 MPa. The joints also exhibited favorable high temperature reliability, maintaining a shear strength of 58.4 MPa at 1000 °C. Microstructural analysis following thermal exposure revealed partial coarsening of primary blocky phases and interfacial Al₄C₃ precipitates via Ostwald ripening, which contributed to the reduction in high temperature strength. Nevertheless, the robust retention of fine eutectic clusters ensured satisfactory structural stability under thermal loading. This work provides a viable strategy for designing Si-based brazing fillers for high-performance SiC ceramic joining.