Ceramic/metal composite materials were widely used in national defense, military industry, and aerospace fields as lightweight impact-resistant structures with high specific strength and high energy absorption efficiency. With the development of 3D printing technology, it has become possible to fabricate complex lattice structures based on triply periodic minimal surfaces (TPMS). In this paper, an interpenetrating TPMS ballistic composite structure composed of silicon carbide (SiC) ceramic and titanium alloy (TC4) is designed. A series of numerical simulations are carried out under single-projectile and double-projectile penetration conditions using ABAQUS software. The damage modes, penetration depth, and ballistic limit velocity of the proposed structure and pure SiC target plate are compared and analyzed. The simulation results show that different interpenetrating TPMS structures exhibit distinct damage and failure modes. The three-dimensional topological configuration restrains crack propagation inside the ceramic, resulting in slighter overall damage than the pure SiC target plate. The damage caused by the second projectile further propagates along the penetration region of the first projectile, and accompanied by an increase in penetration depth. Compared with the pure SiC target plate, the three interpenetrating TPMS targets present smaller penetration depth and higher ballistic limit velocity. When the projectile can perforate the target plate, the primitive (P-type) structure shows better ballistic performance against low-velocity projectiles, while the diamond (D-type) structure is superior against high-velocity projectiles. It is demonstrated that the interpenetrating TPMS targets possess better ballistic performance than pure SiC at the same area density. The technical support and theoretical basis can be provided for the design of novel lightweight ceramic armor in this study.

Strut-based lattice metamaterials are a category of ultra-lightweight, load-bearing, and energy-absorbing materials with broad application prospects in fields such as impact protection, aerospace engineering, and lightweight structural design. Benefiting from their unique periodic architectures and adjustable meso-structural parameters, these materials exhibit exceptional mechanical tunability and multifunctional potential. However, due to the extensive parameter space of mesoscopic configurations and the highly nonlinear correlation between the structural geometry and the mechanical response, the optimization of mechanical performance for lattice metamaterials remains a formidable challenge. Based on the meso-structural characteristics of strut-based lattice metamaterials, an efficient rapid digital modeling method was proposed. A Python script coupled with Abaqus software was utilized for the rapid modeling of truss lattice metamaterials and fast calculations about the mechanical properties of the metamaterials. Based on the calculation results, a machine learning dataset was constructed. Three types of truss lattice structures were randomly selected and additively manufactured. Quasi-static compression tests on these three lattice structures were conducted using a universal testing machine to verify the reliability of the dataset. Subsequently, an artificial neural network (ANN) was trained to rapidly predict the mechanical properties of the truss lattice metamaterials. Focusing on the load-bearing capacity, energy absorption capability, and the concurrent optimization of both, a non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was employed. The well-trained ANN served as a surrogate model embedded within NSGA-Ⅱ. Lattice configurations that exhibited high load-bearing capacity and superior energy absorption characteristics were generated by the optimization process. These configurations also achieved a balance between load-bearing and energy-absorption performance, facilitating the optimization design of truss lattice metamaterials. Additionally, simulation validations confirmed the reliability of the optimization outcomes, demonstrating the effectiveness of integrating ANN with evolutionary algorithms for the advanced design of metamaterials. By integrating machine learning with numerical simulations, the computational cost of optimization design was effectively reduced, offering support for the rapid performance optimization and customized design of complex lattice metamaterials.