Abstract: In the process of fabricating metal 3D lattice structure with additive manufacturing technology, many small defects such as adhesion and fracture often occur in the structure, which leads to the structural function decline of the sample. According to the characteristic difference between defective structure and normal structure, an automatic identification method for small defects in metal lattice structure is proposed. The original input images are obtained by scanning metal lattice structures with X-ray microfocusing CT, and the original feature extraction network is improved based on the Faster R-CNN(Faster region-based convolutional neural networks) framework. The image super-resolution reconstruction module is developed. By enhancing the local detail features of industrial CT images, it can quickly and effectively identify the types of small defects and mark the location information of defects. Experimental results show that the improved Faster R-CNN model has an average correct rate of 93.5% for identifying two typical small defects in metal lattice structure samples. The results show that the feature extraction of small defects can be improved by using super-resolution network to enlarge the image, and the feature learning can be enhanced by deepening the network, thus realizing the automatic identification of small defects in the lattice structure.

Key words: metal lattice structure, defect identification, CT slices, improved Faster R-CNN, super-resolution reconstruction