With the rapid development of vision sensing, artificial intelligence, and robotics technology, one of the challenges we face is installing more advanced vision sensors on welding robots to achieve intelligent welding manufacturing and obtain high-quality welding components. Depth perception is one of the bottlenecks in the development of welding sensors. This review provides an assessment of active and passive sensing methods for depth perception and classifies and elaborates on the depth perception mechanisms based on monocular vision, binocular vision, and multi-view vision. It explores the principles and means of using deep learning for depth perception in robotic welding processes. Further, the application of welding robot visual perception in different industrial scenarios is summarized. Finally, the problems and countermeasures of welding robot visual perception technology are analyzed, and developments for the future are proposed. This review has analyzed a total of 2662 articles and cited 152 as references. The potential future research topics are suggested to include deep learning for object detection and recognition, transfer deep learning for welding robot adaptation, developing multi-modal sensor fusion, integrating models and hardware, and performing a comprehensive requirement analysis and system evaluation in collaboration with welding experts to design a multi-modal sensor fusion architecture.

During steel production, various defects often appear on the surface of the steel, such as cracks, pores, scars, and inclusions. These defects may seriously decrease steel quality or performance, so how to timely and accurately detect defects has great technical significance. This paper proposes a lightweight model based on multi-branch dilated convolution aggregation and multi-domain perception detection head, DAssd-Net, for steel surface defect detection. First, a multi-branch Dilated Convolution Aggregation Module (DCAM) is proposed as a feature learning structure for the feature augmentation networks. Second, to better capture spatial (location) information and to suppress channel redundancy, we propose a Dilated Convolution and Channel Attention Fusion Module (DCM) and Dilated Convolution and Spatial Attention Fusion Module (DSM) as feature enhancement modules for the regression and classification tasks in the detection head. Third, through experiments and heat map visualization analysis, we have used DAssd-Net to improve the receptive field of the model while paying attention to the target spatial location and redundant channel feature suppression. DAssd-Net is shown to achieve 81.97% mAP accuracy on the NEU-DET dataset, while the model size is only 18.7 MB. Compared with the latest YOLOv8 model, the mAP increased by 4.69%, and the model size was reduced by 23.9 MB, which has the advantage of being lightweight.

The emergence of antibiotic‐resistant bacteria poses a significant challenge to the prompt and appropriate treatment of pathogenic bacteria infections, such as acute bacterial skin and skin‐structure infections (ABSSSI), especially in the presence of biofilms. Bacterial biofilms are naturally resistant to antibiotics and the human immune system, making biofilm‐based infections extremely difficult to treat. Therefore, developing new antibacterial therapies targeting biofilms is crucial. Aggregation‐induced emission luminogens with fluorescence in the second near‐infrared window (NIR‐II AIEgens), which can be activated by a near‐infrared laser to generate heat, offer an effective and precise photothermal therapy (PTT) approach for treating deep‐tissue bacterial infections. However, the presence of biofilms impedes the entry of photosensitizers into the infected area, requiring higher drug doses and increasing the risk of PTT. Herein, we developed a biocompatible AIEgen‐based biohybrid nano formulation that incorporates the BPBBT (NIR‐II AIEgen) and antibiofilm α‐amylase into a red blood cell (RBC) membrane‐derived nanovesicle carrier for a PTT/biofilm degradation combination therapy. The synergistic effect of this new formulation enhances both the photothermal capability of BPBBT and the biofilm degradation compared to traditional individual treatments. The new combination therapy demonstrated significant improvement in treating severe Staphylococcus aureus infections caused by biofilms in vitro and in vivo, presenting a promising alternative to traditional antibiotic therapy.

The laser parameters and additive materials were investigated for the laser–additive manufacturing of titanium alloys. A pre–placed metal–strip–based method for 3D printing titanium components was proposed. Before laser processing, the metal strips were fixed to a Grade 5 titanium substrate using resistance spot welding. The effect of the processing conditions (laser power: 1.5–3.0 kW; scanning speed: 3–9 mm·s⁻¹; one or three layers; 1, 6, or 12 passes; Grade 2 and Grade 5 as additives) on the microstructures, oxidation resistance, and wear resistance of the as–printed samples was investigated. The results showed that the microstructure consisted of α′ martensite, oxygen–stabilized α, transformed β that contains coarse and fine acicular α, titanium oxides, and carbides. Cracks were observed, particularly near the upper surface of the three–layer samples. The cracks were suggested to have formed due to cumulative residual stresses and the formation of oxygen–stabilized α and α′ martensite that might cause embrittlement. Both oxidation and wear tests were conducted to verify the improved performance. After 55 h of oxidation, the as–printed samples showed mass gains of 0.029–0.035 g·cm⁻², which were smaller than those of the substrate (0.039 g·cm⁻²).

Many common DTN routing protocols are replication-based, which have relatively good performance in terms of message delivery ratio but high overhead, and leave the issue of garbage collection open. In this paper, we propose Named Data Distance Routing (NDDR), a named data based DTN routing approach which makes routing decisions for named data based on topological distance information. This helps to reduce the overhead of routing. We have implemented NDDR in the ONE simulation environment and the simulation results show that the proposed routing method has better performance in terms of message delivery ratio and network overhead compared with several typical replication-based DTN routing protocols.

We proposed an efficient method of direction of arrival (DOA) estimation for coherent signal based on sparse signal reconstruction. For the inter-atom interference of redundant dictionary affecting the feasibility of sparse signal reconstruction, in this paper, our method for the DOA estimation of coherent signal is based on regularized orthogonal matching pursuit (ROMP) algorithm by designing adaptive sensing dictionary to mitigate the inter-atom interference (AMIAI). Simulated results demonstrate effectiveness of the method by plotting spatial spectrum, by comparing the rootmean-square error (RMSE) of some methods and Cramér-Rao Lower Bound (CRLB) and by comparing the running time of some methods. Our approach makes effort to improve the estimation performance of algorithm and has overload capability.