The high topological silicon carbide (SiC) ceramics can be prepared by stereolithography (SLA) combined with liquid silicon infiltration (LSI) techniques. This paper aims to enhance the performance of SiC ceramics prepared by SLA and LSI techniques via the cyclic impregnation/carbonization of the precursor of carbon source solution before LSI. The effects of impregnation/carbonization cycles on the microstructure and properties of C/SiC preform and sintered body were analyzed in detail. The results show that, with the increase of impregnation/carbonization cycles, the porosity in the C/SiC preform decreases obviously and the content of secondary SiC in the sintered body increases effectively. Especially, when the impregnation/carbonization cycle was performed twice, the sintered body had the optimal mechanical properties. The value of flexural strength, bulk density and elastic modulus were 258.63 ± 8.33 MPa, 2.95 ± 0.02 g/cm³ and 425.16 ± 14.15 GPa, respectively. In addition, the thermal dimensional stability of sintered body was also improved by this method. This method proves that SiC ceramics prepared by SLA combined with LSI have the potential of applications in space optical mirrors.

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.

Complex structure reaction-bonded silicon carbide (RB-SiC) can be prepared by reactive melt infiltration (RMI) and digital light processing (DLP). However, the strength and modulus of RB-SiC prepared by DLP are not sufficient, due to its low solid content (around 40 vol.%), compared with the traditional fabrication techniques (solid content > 60 vol.%). With this understanding, a new method to improve the properties of RB-SiC was proposed, by the impregnation of composite precursor into the porous preform. The composite precursor was composed of phenolic (PF) resin and furfuryl alcohol (FA). PF and FA were pyrolyzed at 1850 °C to obtain amorphous carbon and graphite into the porous preform, respectively. The effects of multiphase carbon on the microstructure and performance of RB-SiC was studied. When the mass ratio of PF to FA was 1/4, the solid content of RB-SiC increased from 40 vol.% to 68.6 vol.%. The strength, bulk density and modulus were 323.12 MPa, 2.94 g/cm³ and 348.83 Gpa, respectively. This method demonstrated that the reaction process between liquid Si and carbon could be controlled by the introduction of multiphase carbon into the porous preforms, which has the potential to regulate the microstructure and properties of RB-SiC prepared by additive manufacturing or other forming methods.

In the gel-casting process, the proper selection of technological parameters is crucial for the final quality of a green body. In this work, the finite element method is used to investigate the mold characteristics in the gel-casting process, and the typical flow behaviors under different conditions are presented. Based on the distribution characteristics of temperature, pressure and flow field of gel polymer, the simulated results provide some possible reasons for the generation mechanisms of defects. Then, a series of simulations were performed to investigate the effect of process parameters on the molding quality of green gel-cast bodies. The results show that the decreasing loading speed can effectively reduce the number of defects and improve the molding quality. In addition, this paper presents a new technique by applying the exhaust hole to decrease the number of defects and, hence, improve structural integrity. The influence of the loading speed on the mold characteristics is well understood for the gating system with an exhaust hole, which suggests to us appropriate parameters for optimizing the molding design. This work provides a theoretical basis to explicate the generating mechanism of defects involved in the gel-casting process and acquires an optimized technique to produce a silicon carbide green body.