## 4 Discussion
Thus, the continuous orientation formulation directs the material deposition path planning to ensure the fiber trajectory curvature, fiber continuity, fiber fraction, and offset distance between adjacent fibers, unlike discrete methods, where fiber convergence and fiber continuity are challenging to attain. [PERSON] et al. [99] designed the topology and material orientation of parts simultaneously; the optimized results were post-processed using continuous fiber path planning to ensure realizability. A sequential scheme was proposed [144; 13] where the fiber placement based on load transmission follows isotropic TO; this is contrary to [PERSON] [145], who adopted concurrent fiber path planning and structural topology optimization. The multi-axis material deposition technology using a robotic arm requires an extension of the TO algorithm to envelop the 3D fiber orientation, in contrast to in-plane printing. [PERSON] et al. [146] introduced azimuth and elevation angles to extend the CFO method for 3D fiber orientation. In addition, they emphasized the issues of the non-convexity of the compliance and sensitivity to the initial fiber orientations by investigating the orientation parameter space to mitigate these problems [147]. Finally, the realizability of 3D-printed composite is studied by [PERSON] et al. [148], where they proposed a filtering technique for fast convergence. Utilizing TO methods for exploring CF4 technology, generally speaking, heightens the composite manufacturing cost, especially when applying these technologies in the production of large-scale structure parts. Therefore, understanding the trade-off among commercial aspects, i.e., realizability, practicability, and structural design, requires assimilating the benefits of the discrete, continuous, and multi-component methodologies. Thus, a discrete-continuous parameterization scheme optimizes the structural topology and material orientation, including multi-component optimization (MTO) that decomposes product geometry while guaranteeing manufacturing constraints that might significantly impact the quality and cost of the end product. Initially, a genetic algorithm was used to solve MTO [149], and recently, a gradient-based optimization algorithm was used by [PERSON] et al. [150]. [PERSON] et al. [151] further extended their work for structures made of multiple composite components with tailored material orientations without a prescribed set of alternative discrete angles. Therefore, these methods can produce regions fabricated separately and joined with either continuous or discrete material orientation methods, or both, as considered in [PERSON]'s work [91]. Feature-based parameterization follows the ideology of ready-for-manufacturability with a necessary restriction on the spatial distribution of the fiber orientations. It envelops commercial aspects for the realizability of composite parts by introducing CAD-based features to ease the manufacturing process with the potential for layerwise design. Moreover, it further simplifies the design space by considerably reducing design variables. It is noted that published works only considered stiffness-driven design. However, it is also critical to consider failure modes for composite parts manifested using the layerwise AM process. These failure criteria render markedly different designs that raise the method's relevance in fabricating FRC structures. Figure 7 depicts optical microscopy images of the cross-section of the fiber-reinforced plastic material, namely, carbon fibers impregnated with nylon plastic (polyamide 6), which is wound on the filament spool placed in the material chamber, making a somewhat circular cross-section. However, the filament cross-section takes a rectangular shape once it is deposited on the plate. This is because the carbon fiber bundle filament passes through  
the roller before it is deposited on the platen. In addition, [PERSON] et al. [15] demonstrated the effects of voids on the failure mechanism due to poor fiber-matrix interfaces, causing reduced mechanical performance.