In Wire Arc Additive Manufacturing (WAAM), solidification grain morphology in titanium alloy tends to be columnar rather than equiaxed due to heat dissipation and repeated thermal cycles. This study demonstrates improved microstructure and anisotropic properties in Ti-6Al-4V specimens fabricated by WAAM and treated with hammer peening, resulting in a transition from columnar grains to fine equiaxed grains (~300 μm) in both single-pass and four-bead WAAM walls. The anisotropic elongation decreased by approximately 7%, and tensile strength along the building direction decreased by ~50 MPa for a single-pass wall. Additionally, small and large equiaxed prior-β grains appeared alternately due to the combined effect of hammer peening and welding deposition. The region can be categorized into three parts (MAX, MED, MIN) based on the degree of plastic strain characterized by KAM mapping of EBSD data. In current WAAM parameters, the ratio of strong (~1.5 mm) deformation field (MAX) is about 50% within one deposition layer (MAX+MIN), suggesting a new approach for producing equiaxed prior-β grains. We expect that this method will be applicable for transforming the prior-β grains from columnar to equiaxed. Furthermore, the distribution of plastic strain and phase transformation mechanisms offers innovative approaches to optimize the hammer peening process, with potential applications to optimize the process for more complex components in the aerospace and power plant industries.

The precise identification of wheat seedlings in unmanned aerial vehicle (UAV) imagery is fundamental for implementing precision agricultural practices such as targeted pesticide application and irrigation management. This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from variable illumination conditions and (2) morphological characteristics of wheat seedlings characterized by slender leaf structures and flexible posture variations. To address these challenges, we propose DHS-YOLO, a novel deep learning framework optimized for robust wheat seedling detection under diverse illumination intensities. Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. Second, the Histogram Transformer (HT) module integrates a dynamic-range spatial attention mechanism to mitigate illumination-induced image degradation. Third, we implement the ShapeIoU loss function that prioritizes geometric consistency between predicted and ground truth bounding boxes, particularly optimizing for slender plant structures. The experimental validation was conducted using a custom UAV-captured dataset containing wheat seedling images under varying illumination conditions. Compared to the existing models, the proposed model achieved the best performance with precision, recall, mAP50, and mAP50-95 values of 94.1%, 91.0%, 95.2%, and 81.9%, respectively. These results demonstrate our model’s effectiveness in overcoming illumination variations while maintaining high sensitivity to fine plant structures. This research contributes an optimized computer vision solution for precision agriculture applications, particularly enabling automated field management systems through reliable crop detection in challenging environmental conditions.

Air suspension components are critical elements of automotive chassis and are commonly fabricated by welding 6061-T6 aluminum using 4043 filler wire with the cold metal transfer (CMT) process. Variations in vehicle architecture necessitate different groove angles and matching parameter windows. This study aims to elucidate how groove angle and heat input govern weld quality to inform process optimization. Two groove angles (120° and 90°) were investigated under distinct heat-input conditions (denoted 120-H and 90-L). Characterization covered chemical composition, macroscopic morphology, porosity, microstructure, hardness, and mechanical properties. The key novelty lies in elucidating the relationship between liquation cracking and metal flow lines, which jointly govern crack propagation. Integrating evidence from porosity measurements, crack characterization, and numerical simulations indicates that the 120-H parameter set requires further optimization. Overall, the results underscore the pivotal roles of groove angle and heat input in CMT welding of 6061-T6 aluminum and provide a basis for process parameter optimization in air suspension manufacturing.

As a critical component of rocket systems, the grid fin is widely applied in the aerospace industry. Compared to traditional manufacturing methods and other additive manufacturing (AM) techniques, wire arc additive manufacturing (WAAM) is more advantageous in its time and cost efficiency, especially when it is utilized in the large-scale production of components. Given the significant effect of the welding strategies on the quality of manufactured parts, we investigated two distinct WAAM printing orientations, horizontal, or lie (L), and perpendicular, or stand (S), using a small-scale model. The feasibility of these two printing approaches was evaluated by analyzing the surface quality, microstructure, and mechanical properties at the junctions of the grid fin. Furthermore, a finite element analysis (FEA) was adopted to simulate and analyze the main factors, including the temperature distribution, deformation, and stress profiles at the welding joints, in both AM strategies. The integrated approach adopted in this study provides important insights for optimizing the application of WAAM in grid fin manufacturing. In summary, our results indicate that while the L mode is easily manufacturable and exhibits stable properties, the S mode holds significant market potential if its welding parameters are optimized.