Resin Transfer Molding (RTM) is a process in which a thermoset polymer is injected into a closed mold filled with dry fabric reinforcement. Achieving net-shaped, void-free composite parts in RTM depends on using well-designed molds that promote uniform resin flow throughout the fiber preform. Traditionally, the design and fabrication of RTM molds, including the placement of injection gates and vent channels, involves an expensive and iterative process. This study investigates the integration of additive manufacturing (AM) techniques for the rapid prototyping of RTM molds with embedded sensing capabilities for real-time monitoring of the resin infusion process. In this work, carbon nanotube (CNT)-coated veils are incorporated as in-situ sensors within the AM-fabricated molds. The electrical conductivity of veils changes in response to resin infiltration, allowing for the detection of resin arrival and saturation at predefined locations within the mold. Experimental sensor data acquired during RTM trials are systematically compared with numerical predictions from resin flow simulations. Results show that the sensor signals provide local measurements of resin arrival times, supporting the feasibility of AM-enabled, sensor-integrated molds for RTM process monitoring and potential real-time control.

Functionally graded lattice structures (FGLSs) have emerged as a promising class of architected materials that enable spatial control of mechanical, thermal, and biological properties through variations in geometry, topology, material composition, and density. Advances in additive manufacturing (AM) have significantly expanded the design and fabrication capabilities of FGLSs for applications in aerospace, biomedical, energy, and transportation sectors. This review presents a comprehensive assessment of FGLSs through a process–structure–property (P–S–P) perspective, highlighting the influence of AM processes, manufacturing defects, and microstructural evolution on structural performance. A unified six-axis taxonomy (topology family, graded variable, gradient-field description, scale level, process and material route, target metric) is proposed for cross-study comparison. Five architecture families are examined: strut-based, triply periodic minimal surface (TPMS), stochastic, plate-based, and mechanism-driven or hierarchical lattices, together with density, material, topology, and hybrid grading approaches. Recent advances in computational modeling, topology optimization, artificial intelligence (AI), and machine learning are discussed, emphasizing their role in accelerating design, property prediction, and inverse engineering. Finally, key challenges and future opportunities related to digital twins, autonomous manufacturing, multi-material systems, and qualification of FGLSs are identified. This review provides a structured framework for the development of next-generation AI-enabled FGLSs with enhanced multifunctional performance and manufacturability.