Additive Manufacturing of Functionally Graded Lattice Structures: Process–Structure–Property Relationships, Design Strategies, and Future Perspectives

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.