Additive friction stir deposition (AFSD), in which molten metal materials are formed into free-form stacked structural parts according to the path design, may have a wide range of applications in high-efficiency mass production. In this study, experiments were conducted for the rotational speed in the AFSD parameters of 6061 aluminium alloy bars to investigate the effects of different rotational shear conditions and heat inputs on the properties of the deposited layer for diameter bars based on the analysis of the micro-morphology, micro-tissue composition, and mechanical properties. The width and thickness of each layer were constant, approximately 40 mm wide and 2.5 mm thick. The particle undulations on the surface of the deposited layer were positively correlated with the AFSD rotational speed. Continuous dynamic recrystallisation in the AFSD process can achieve more than 90% grain refinement. When the rotational speed increases, it causes localised significant orientation and secondary deformation within the recrystallised grains. The ultimate tensile strength of the deposited layer was positively correlated with the rotational speed, reaching a maximum of 211 MPa, and the elongation was negatively correlated with the rotational speed, with a maximum material elongation of 37%. The cross-section hardness of the deposited layer was negatively correlated with the number of thermal cycles, with the lowest hardness being about 45% of the base material and the highest hardness being about 80% of the base material.

In this paper, a kinematic separation calibration method of 6R series manipulator is proposed, and its absolute accuracy is improved by a binocular camera and standard sphere. First, a geometric error mapping model for the robotic arm was established, and the error parameters were divided into position parameters and attitude parameters for calibration purposes. Second, in the process of solving error parameters using numerical algorithms, it is easy to encounter matrix ill-conditioned problems. The spectral correction iteration method is introduced to improve the calculation accuracy. Third, three standard balls are installed at the end of the robotic arm as markers, and the center coordinates are measured using a binocular camera to obtain the actual end pose parameters. To verify the effectiveness of the proposed method, a simulation model verification was designed, and the results showed that the separation calibration method was the best. Finally, the IRB-1200 robot was successfully calibrated using the proposed method; the average robot position and angle error after calibration was significantly decreased. The position accuracy was improved by 66.9%, and the attitude accuracy was improved by 86.2%.

Cracks are one of the main types of tunnel-lining defects. At present, there are no particularly good methods for identifying tunnel-lining cracks. The methods that are used are associated with problems such as poor robustness, low detection efficiency, and inconsistency in defect identification. Vision-based crack identification algorithms that use images of tunnel-lining cracks are also affected by problems such as weak features, irregular shapes, random development, and small proportions. In light of this, we propose in this paper a neural network that integrates U-net and FPN (feature pyramid network) pyramid structure characteristics. Because of its ability to output deep feature information, an SK (selective kernel) attention mechanism was integrated to increase the weight of effective feature regions and improve the detection accuracy of feature maps. An RPN (region proposal network) regional detection network was used for multi-scale regional screening, and non-maximum suppression was used to eliminate repeated anchor frames between different regions. The continuity of cracks was determined using adaptive regional expansion. The region segmentation network was also combined with multi-scale feature maps. Finally, the segmentation results for regions of fine features could be mapped to the whole image to identify cracks, thereby solving the problem that arises when textural features of cracks are weak and cannot be accurately identified. For testing purposes, photos of tunnel-lining crack defects were used. The algorithm described in this paper was able to combine deep and shallow features to identify more abundant crack features, and the recognition accuracy of the crack classification network reached 98.51%. With respect to crack segmentation, the algorithm’s segmentation accuracy reached 94.55%, and its single processing time reached 60.76 ms, indicating more accurate and efficient segmentation performance, compared with FCNs (fully convolutional networks) and classical U-net networks.

Screw assemblies of radar are characterized by numerous densely distributed small screws of various types, rendering their manual assembly difficult and inefficient. To improve the assembly quality and efficiency, an automatic assembly system was developed. First, the position and attitude relationships among the workpiece, camera, and base coordinate systems of the manipulator were obtained using three-point location and nine-point calibration methods. Second, a visual servoing strategy is designed for guiding the manipulator such that its position can be adjusted adaptively to achieve the high-precision positioning of threaded holes. Third, a method for evaluating assembly quality is developed. The templates of angle–torque curves of various screws were produced by fitting experimental data. Furthermore, the assembly quality was determined by calculating the similarity between the angle–torque curves and templates based on the Fréchet distance. Finally, the feasibility was verified using a flat plate component with five threaded holes.

At present, most studies in the field of Wire-Friction Stir Additive Manufacturing (W-FSAM) adopt the side wire feeding method. However, the side wire feeding method has problems in that the wire feeding tube occupies working space and the tool is prone to clogging. To address this, this study proposes a Coaxial Wire Feeding-Friction Stir Additive Manufacturing (CWF-FSAM) method. The CWF-FSAM device adopts a structure where a fixed shaft is coaxially nested inside the stirring shaft, and the fixed shaft is machined with through-channels along the circumferential direction for wire feeding, which eliminates the limitation of the wire feeding tube. This study elaborates on the structure of the CWF-FSAM device, then uses 6061 aluminum alloy as the deposition material for additive manufacturing, and conducts characterization and analysis on the microstructure and mechanical properties of the deposited components. The results show that the interlayer bonding of the deposited components is dense without defects. The components exhibit uniform and fine equiaxed grains, with the average grain sizes of the top, middle, and bottom parts being 3.52 µm, 3.35 µm, and 4.07 µm, respectively. In terms of mechanical properties, the tensile strengths of the components along the building direction (BD) and longitudinal direction (LD) both reach 70% of that of the base material (BM) wire. The hardness ranges from 36 HV to 42 HV. In addition, closed-loop components were prepared by continuous counterclockwise deposition using the CWF-FSAM device. The tensile strengths of the overlapping area, straight section, and corner were 124.45 MPa, 125.88 MPa, and 126.95 MPa, respectively. The overall performance of the closed-loop components is uniform and stable, which indicates that the CWF-FSAM-deposited components have good mechanical property isotropy.