Additive manufacturing (AM) is widely used in industries such as aerospace, medical, and automotive. Within this domain, defect detection technology has emerged as a critical area of research focus in the quality inspection phase of AM. The main challenge lies in that under extreme lighting conditions, strong reflected light obscures defect feature information, leading to a significant decrease in the defect detection rate. This paper introduces a novel methodology for intelligent defect detection in AM components with reflective surfaces, leveraging virtual polarization filtering (IEVPF) and an improved YOLO V5-W model. The IEVPF algorithm is designed to enhance image quality through the virtual manipulation of light polarization, thereby improving defect visibility. The YOLO V5-W model, integrated with CBAM attention, DenseNet connections, and an EIoU loss function, demonstrates superior performance in defect identification across various lighting conditions. Experiments show a 40.3% reduction in loss, a 10.8% improvement in precision, a 10.3% improvement in recall, and a 13.7% improvement in mAP compared to the original YOLO V5 model. Our findings highlight the potential of combining virtual polarization filtering with advanced deep learning models for enhanced AM surface defect detection.

Aiming at the key technology of defect detection in precision additive manufacturing of highly reflective metal materials, this study proposes an enhanced SCK-YOLOV5 framework, which combines polarization imaging and deep learning methods to significantly improve the intelligent identification ability of small metal micro and nano defects. This framework introduces the SNWD (Selective Network with attention for Defect and Weathering Degradation) Loss function, which combines the SIOU Angle Loss with the NWD distribution sensing characteristics. It is specially designed for automatic positioning and identification of micrometer hole defects. At the same time, we employ global space construction with a dual-attention mechanism and multi-scale feature refining technique with selection kernel convolution to extract multi-scale defect information from highly reflective surfaces stably. Combined with the polarization imaging preprocessing and the comparison of enhancement defects under high reflectivity, the experimental results show that the proposed method significantly improves the precision, recall rate, and mAP50 index compared with the YOLOv5 baseline (increased by 0.5%, 1.2%, and 1.8%, respectively). It is the first time that this improvement has been achieved among the existing methods based on the YOLO framework. It creates a new paradigm for intelligent defect detection in additive manufacturing of high-precision metal materials and provides more reliable technical support for quality control in industrial manufacturing.

Additive manufacturing (AM) technology has found extensive applications in aerospace, medical, and automotive fields. Defect detection technology remains a research focus in AM process monitoring. While machine learning and neural network algorithms have recently achieved significant advancements in innovative applications for AM defect detection, practical implementations still face challenges, including insufficient detection accuracy and poor system robustness. To address these limitations, this study proposes the YOLOv5-CAD defect detection model. Firstly, the convolutional block attention module (CBAM) is introduced into the core feature extraction module C3 of the backbone network to enhance attention to critical information and improve multi-scale defect target adaptability. Secondly, the original CIoU loss function is replaced with the Alpha-IoU loss function to accelerate network convergence and strengthen system robustness. Additionally, a fully decoupled detection head substitutes the original coupled head in the YOLOv5s model, separating the object classification and bounding box regression tasks to improve detection accuracy. Finally, a polarization technology-based visual monitoring system is developed to acquire defect images of laser AM workpieces, establishing the model’s training sample database. Compared with YOLOv5, the proposed model demonstrates a 2.5% improvement in precision (P), 2.2% enhancement in recall (R), 3.1% increase in mean average precision (mAP50), and 3.2% elevation in mAP50-95. These quantitative improvements confirm the model’s capability to provide robust and real-time technical solutions for industrial AM quality monitoring, effectively addressing current limitations in defect detection accuracy and system reliability.

Laser additive manufacturing (AM) technology has become an important method for the manufacturing of high-performance aluminum alloy parts. However, the thermal effect of the molten pool and the defect formation mechanism are still the key issues restricting forming quality. To address this issue, this paper systematically investigates the effects of key parameters such as laser power and pulse frequency on the thermal conductivity, kinetic behavior, and defect control of the molten pool through multi-physics coupled numerical simulation to provide theoretical support for improving the quality of components. It is found that the laser power and pulse frequency play a key role in the molten pool morphology and defect generation, with too low a power leading to non-fusion and too high a power triggering overheating and cracking, and too low a frequency leading to unstable morphology and too high a frequency triggering grain coarsening and thermal stress cracking. The optimized process parameters (power 700–800 W, frequency 72–100 KHz) effectively improved the melt pool morphology and reduced the defects. This study reveals the intrinsic mechanism of melt pool dynamics and defect formation, which provides important instructions for optimizing the aluminum alloy additive manufacturing process.

The ability to freely manipulate the wavefronts of surface plasmon polaritons (SPPs) or surface waves (SWs), particularly with multifunctional integration, is of great importance in near-field photonics. However, conventional SPP control devices typically suffer from low efficiency and single-function limitations. Although recent works have proposed metasurfaces that achieve bifunctional SPP manipulation, their implementation relies on the excitations of circularly polarized (CP) light with different helicities. Here, we propose a generic approach to designing bifunctional SPP meta-devices under single-helicity circularly polarized incidence. Constructed using carefully selected and arranged meta-atoms that possess both structural resonance and a geometric phase, this kind of meta-device can exhibit two distinct SPP manipulation functionalities in both co- and cross-polarized output channels under one CP incidence. As proof of this concept, we designed a bifunctional meta-device in the microwave regime and numerically demonstrated that it can convert a normally incident left circularly polarized (LCP) beam into SWs, exhibiting both a focused wavefront in the co-polarized output channel and a deflected wavefront in the cross-polarized output channel. Our findings substantially enrich the capabilities of metasurfaces to manipulate near-field electromagnetic waves, which can find many applications in practice.