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.

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.