Continuous fiber reinforced composites have excellent mechanical properties, but traditional processes are costly, complex to form, have a long production cycle, and are only suitable for relatively simple structures, which limits the wider application of the material. Fuse Deposition Modeling(FDM) 3D printing has the advantages of simple process, fast molding, low cost, and the ability to manufacture high-complexity structures, which provides the possibility of low-cost and rapid prototyping of composite materials. In this paper, several FDM 3D printing processes for continuous fiber composites are reviewed, and the effects of some related process parameters on the properties of the composites are analyzed. Finally, the problems of FDM 3D printing continuous fiber reinforced composites are pointed out.

Vat photopolymerization is renowned for its high flexibility, efficiency, and precision in ceramic additive manufacturing. However, due to the impact of random defects during the recoating process, ensuring the yield of finished products is challenging. At present, the industry mainly relies on manual visual inspection to detect defects; this is an inefficient method. To address this limitation, this paper presents a method for ceramic vat photopolymerization defect detection based on a deep learning framework. The framework innovatively adopts a dual-branch object detection approach, where one branch utilizes a fully convolution network to extract the features from fused images and the other branch employs a differential Siamese network to extract the differential information between two consecutive layer images. Through the design of the dual branches, the decoupling of image feature layers and image spatial attention weights is achieved, thereby alleviating the impact of a few abnormal points on training results and playing a crucial role in stabilizing the training process, which is suitable for training on small-scale datasets. Comparative experiments are implemented and the results show that using a Resnet50 backbone for feature extraction and a HED network for the differential Siamese network module yields the best detection performance, with an obtained F1 score of 0.89. Additionally, as a single-stage defect object detector, the model achieves a detection frame rate of 54.01 frames per second, which meets the real-time detection requirements. By monitoring the recoating process in real-time, the manufacturing fluency of industrial equipment can be effectively enhanced, contributing to the improvement of the yield of ceramic additive manufacturing products.