Maroua Jabeur, Slim Souissi
ABSTRACT Fused Deposition Modeling (FDM) has evolved from rapid prototyping to a viable engineering manufacturing process. However, the mechanical, thermal, and tribological performance of printed components remains highly sensitive to processing conditions and material behavior. The layer‐by‐layer deposition mechanism induces anisotropic mesostructures characterized by interlayer interfaces, porosity, and residual stresses, making part properties strongly dependent on the coupling between process parameters and material characteristics. This review critically synthesizes current research on process parameter optimization and material selection in FDM within a unified Process–Structure–Property–Optimization framework. The influence of key deposition variables—including layer thickness, extrusion temperature, raster orientation, infill characteristics, and print speed—on macroscopic performance is examined alongside the structure–property implications of commodity polymers, engineering thermoplastics, and composite systems. Prevailing optimization methodologies, such as Design of Experiments, response surface approaches, and multi‐objective strategies, are comparatively analyzed. The review identifies persistent limitations, including fragmented performance evaluation and insufficient integration of material–process interactions. Advancing FDM toward predictive and industrially scalable manufacturing requires physically informed, robust, and integrated optimization frameworks.