## 5 Fiber-Reinforced Composites
(2019) [73] validated finite element models predicting residual stresses and deformation in printed composites but identified limitations in modeling layer adhesion failures, especially for large-scale parts. [PERSON] et al. (2014) [67] experimentally demonstrated high fiber alignment in carbon fiber-reinforced ABS composites, significantly enhancing mechanical performance despite increased porosity. These composites matched or exceeded aluminum alloys' specific strength, demonstrating their structural potential. Despite these advancements, real-world applications still face major challenges such as void formation, fiber misalignment, and weak interfacial bonding. [PERSON] et al. (2025) [74] and [PERSON] et al. (2025) [75] reviewed common defect types and process-level strategies to mitigate them, including in situ heating and tailored deposition. However, these techniques offer only partial solutions and often require compensatory adjustments to extrusion temperature, nozzle geometry, or material formulation. These limitations have prompted efforts to more accurately incorporate experimental imperfections into simulation models. For example, [PERSON] et al. (2022) [76] developed and compared two finite element models of ABS-short carbon fiber composites, aiming to capture the effects of interfacial defects and fiber alignment. As shown in Figure 21, Model CM-1, based on virgin ABS properties, significantly overestimated stiffness and strength, while CM-2, which incorporated tensile data from printed specimens, more accurately reflected the nonlinear behavior observed in experiments. This comparison underscores the  
Figure 20: (a) FEA Tsai–Wu failure contour with insets showing 1-direction stress, 2-direction stress, and shear stresses in two selected regions. (b) Close-up of sudden crack formation at failure in the experimental coupon. (c) Micro-computed tomography (\(\upmu\)CT) slices of the failed coupon compared with the corresponding close-up of the design. Adapted from [72]. importance of using real defect-informed material data to improve the predictive reliability of composite FFF simulations. Building upon the need to better capture and control defect formation, recent studies have explored intelligent control strategies during the printing process. [PERSON] et al. (2023) [15] developed a deep learning-assisted framework for real-time defect detection and closed-loop adjustment in the fabrication of continuous fiber-reinforced polymer composites. By integrating sensor data with neural network-based decision models, their system detected anomalies and adaptively modified print parameters to prevent defect propagation. This architecture not only improved process reliability but also demonstrated the feasibility of combining artificial intelligence with physical simulation to enhance control in fiber-reinforced FFF. Taken together, these studies reveal both the potential and the persistent limitations of fiber-reinforced FFF. While predictive models have become increasingly sophisticated, they still struggle to fully capture the coupled effects of fiber orientation, bonding quality, and defect distribution. One of the key remaining gaps is the ability to link microstructural behavior to macroscopic performance. Recent approaches--such as thermal-mechanical models with in situ deformation tracking [77] and hybrid machine learning frameworks that incorporate physical laws [78]--have begun to address this. However, progress is constrained by the lack of standardized validation methods. Moving forward, a combination of high-fidelity simulations, robust experimental datasets, and process-aware material models will be essential to bridge the gap between theory and application in composite FFF.