In recent years, functional ceramic devices have become smaller, thinner, more refined, and highly integrated, which makes it difficult to realize their rapid prototyping and low-cost manufacturing using traditional processing. As an emerging technology, multi-material 3D printing offers increased complexity and greater freedom in the design of functional ceramic devices because of its unique ability to directly construct arbitrary 3D parts that incorporate multiple material constituents without an intricate process or expensive tools. Here, the latest advances in multi-material 3D printing methods are reviewed, providing a comprehensive study on 3D-printable functional ceramic materials and processes for various functional ceramic devices, including capacitors, multilayer substrates, and microstrip antennas. Furthermore, the key challenges and prospects of multi-material 3D-printed functional ceramic devices are identified, and future directions are discussed.

For large space telescopes, the design of lightweight primary mirrors with an acceptable level of optical performance is a challenge. A parametric optimization method based on topology optimization of the basic configuration of the mirror is proposed. A finite-element model of the mirror is generated with linear shell elements, and the optimal distribution of the material is obtained using the continuum topology optimization technique. The lightweight ribs are grouped according to the results of topology optimization results. The design of experiment method is used to pick out the key factors for parametric optimization. The RMS value of the surface shape error of the mirror and the total mass of the mirror are treated as the objective merit functions, and the first-order natural frequency of the mirror is taken as a constraint for parametric optimization. Results show that the local stiffness of the mirror is significantly affected by the thicknesses of the ribs at the corresponding positions. The optimum mirror design obtained using our optimization method is compared with the initial design, and the comparison shows superior optical performance for the optimized mirror.

The thermal design of spacecraft becomes increasingly complicated as various advanced technologies are continuously introduced to the spacecraft. Determining and optimizing the uncertainties of a spacecraft thermal control system through global sensitivity analysis has long been an essential task for thermal engineers. It is a difficult task that relies heavily on engineering experience and is a time-intensive, trial-and-error endeavor that may not even lead to global optimization. Hence, an intelligent optimization strategy based on statistical machine learning for spacecraft thermal design, called IOSML, is proposed. An intelligent batch processing system (IBPS) based on MATLAB, Python, and NX/TMG real-time data interaction is designed. The IBPS uses a surrogate model to reduce the computational cost of global sensitivity analysis while using a detailed thermal mathematical model to maintain accuracy. We combine a Bayesian inference framework with a neural network surrogate spacecraft-thermophysical model that is 100× faster than numerical solvers. This paper first reports on a density-based global sensitivity analysis that evaluates the effect of design parameters on the temperature difference between the complementary metal–oxide–semiconductor and cold screen of the Lehman Alpha Solar Space Telescope detector. From 42 design parameters, the most sensitive four are selected for optimization, and the temperature difference and the boundary temperature are used as the objective function. Adopting IOSML, under no supervision, four design parameters are optimized through the IBPS, and the effectiveness of the algorithm is verified by comparison with traditional methods. Additionally, IOSML is versatile and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.

The thermal design parameters of space telescopes are mainly optimized through traversal and iterative attempts. These optimization techniques are time consuming, rely heavily on the experience of the engineer, bear a large computational workload, and have difficulty in achieving optimal outcomes. In this paper, we propose a design method (called SMPO) based on an improved back-propagation neural network (called GAALBP) that builds a surrogate model and uses a genetic algorithm to optimize the model parameters. The surrogate model of a space telescope that measures the atmospheric density is established using GAALBP and then compared with surrogate models established using a traditional BP neural network and radial-basis-function neural network. The results show that the regression rate of the surrogate model based on the GAALBP reaches 99.99%, a mean square error of less than 2 × 10⁻⁶, and a maximum absolute error of less than 4 × 10⁻³. The thermal design parameters of the surrogate model are optimized using a genetic algorithm, and the optimization results are verified in a finite element simulation. Compared with the design results of the manually determined thermal design parameters, the maximum temperature of the CMOS is reduced by 5.33 °C, the minimum temperature is increased by 0.39 °C, and the temperature fluctuation is reduced by a factor of 4. Additionally, SMPO displays versatility and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.