Digital light processing (DLP) is a crucial additive manufacturing (AM) technique for producing high-precision ceramic components. This study aims to optimize the formulation of Si₃N₄ slurry to enhance both its performance and manufacturability in the DLP process, and investigate key factors such as particle size distribution, photopolymer resin monomer ratios, and dispersant types to improve the slurry’s rheological properties. Through these optimizations, a photosensitive Si₃N₄ slurry with 50vol% solid content was developed, exhibiting excellent stability, and low viscosity (2.48 Pa·s at a shear rate of 12.8 s⁻¹). The effects of gas-pressure sintering on the material’s phase composition, microstructure, and mechanical properties were further explored, revealing that this technique significantly increases the flexural strength of the green sample from (109 ± 10.24) to (618 ± 42.15) MPa. The sintered ceramics exhibited high hardness ((16.59 ± 0.05) GPa) and improved fracture toughness ((4.45 ± 0.03) MPa·m^1/2). Crack trajectory analysis revealed that crack deflection, crack bridging, and the pull-out of rod-like β-Si₃N₄ grains, are the main toughening mechanisms, which could effectively mitigate crack propagation. Among these mechanisms, crack deflection and bridging were particularly influential, significantly enhancing the fracture toughness of the Si₃N₄ matrix. Overall, this research highlights how monomer formulation and gas-pressure sintering strengthen the performance of Si₃N₄ slurry in the DLP three-dimensional printing technique. This work is expected to provide new insights for fabricating complex Si₃N₄ ceramic components with superior mechanical properties.

Fe–Cr–Ni austenitic alloys are extensively utilized in the hot-end components of nuclear light water reactors, turbine disks, and gas compressors. However, their low strength at elevated temperatures limits their engineering applications. In this study, a novel precipitation-strengthened alloy system is developed by incorporating Al and Si elements into a FeCrNi equiatomic alloy. The results indicate that the FeCrNiAl _xSi _x (at%, x = 0.1, 0.2) alloys possess heterogeneous precipitation structures that feature a micron-scale σ phase at the grain boundaries and a nanoscale ordered body-centered cube (B2) phase within the grains. An exceptional strength-ductility synergy across a wide temperature range is achieved in FeCrNiAl_0.1Si_0.1 alloys due to grain refinement and precipitation strengthening. Notably, a yield strength of 693.83 MPa, an ultimate tensile strength of 817.55 MPa, and a uniform elongation of 18.27% are attained at 873 K. The dislocation shearing mechanism for B2 phases and the Orowan bypass mechanism for σ phase, coupled with a high density of nano-twins and stacking faults in the matrix, contribute to the excellent mechanical properties at cryogenic and ambient temperatures. Moreover, the emergence of serrated σ phase and micro-twins in the matrix plays a crucial role in the strengthening and toughening mechanisms at intermediate temperatures. This study offers a novel perspective and strategy for the development of precipitation-hardened Fe–Cr–Ni austenitic alloys with exceptional strength–ductility synergy over a broad temperature range.

Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks. This study proposes an intelligent approach for predicting rock strength and cuttability. A database comprising 132 data sets is established, containing cutting parameters (such as cutting depth and pick angle), cutting responses (such as specific energy and instantaneous cutting rate), and rock mechanical parameters collected from conical pick-cutting experiments. These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies. In addition, rock cuttability is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method, and subsequently identified through machine learning approaches. Various models are compared to determine the optimal predictive and classification models. The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm–optimized backpropagation neural network model, and the optimal model for rock cuttability classification is the radial basis neural network model.