## 2 Literature Review
[19] analyzed the dimensional accuracy of FDM-printed ABS P400 parts using a fuzzy inference system and concluded that part orientation plays a critical role in maintaining precision. This finding was further corroborated by [PERSON] et al. [20], who employed the Taguchi method and an ANN, reaching similar conclusions regarding the significance of part orientation [21]. [PERSON] and [PERSON] [22] explored the correlation between dimensional accuracy and processing cost in FDM-manufactured parts. By applying the desirability function, they optimized fabrication parameters to achieve a balance between precision and cost efficiency. Their study revealed that layer thickness, part inclination, and the interaction between these parameters played a crucial role in influencing both dimensional accuracy and overall production costs, emphasizing the need for careful parameter selection in FDM processes. When a process requires the simultaneous optimization of multiple objective functions, especially when they are conflicting, multi-objective optimization provides a systematic approach to balancing trade-offs and achieving the best possible outcomes. This method enables the identification of optimal parameter combinations that improve overall performance while addressing competing requirements [23]. NSGA-II is one of the most widely used multi-objective optimization algorithms in FDM applications. [PERSON] et al. [24] applied NSGA-II to analyze the effects of layer thickness and part orientation on production time and material usage in the FDM process. Their formulation incorporated surface roughness and tensile strength as constraints to ensure product quality. The optimization process resulted in 18 Pareto-optimal solutions for the two controllable parameters. Several studies have explored different approaches to optimizing FDM process parameters. [PERSON] [11] analyzed the impact of process conditions on processing time, energy consumption, and dimensional accuracy using the Taguchi method and the desirability function. The findings identified layer thickness, road width, and printing plane as the most influential factors. Similarly, [PERSON] et al. [25] applied the Taguchi method to optimize the dimensional accuracy and tolerance of FDM-printed parts, revealing that layer thickness, infill speed, infill shell spacing multiplier, number of shells, and extruder temperature significantly affected the results. Their study also proposed optimized process parameters to enhance dimensional accuracy and geometric characteristics in FDM manufacturing. One of the critical challenges in the fused-deposition modeling (FDM) process is the occurrence of warping deformation, which can significantly impact the dimensional accuracy and mechanical properties of printed parts. Various studies have investigated this phenomenon, proposing models to predict and mitigate its effects. For instance, [PERSON] et al. [26] developed a model to analyze prototype warp deformation in the FDM process, highlighting the influence of material properties, thermal gradients, and layer adhesion on the final part geometry. Their findings suggest that optimizing process parameters such as printing temperature, layer thickness, and cooling rates can reduce deformation and improve print quality. [PERSON] et al. [27] investigated the dimensional accuracy of parts manufactured using fused-deposition modeling (FDM), employing a methodology based on analyzing the relationships between process parameters and dimensional deviations of ABS and ULTRAT parts. The authors applied grey relational analysis (GRA) combined with signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) to determine the optimal combination of process parameters that minimize dimensional deviations. The results indicated that part orientation and layer thickness had a significant influence on dimensional accuracy, and experimental validation confirmed that selecting the optimal manufacturing parameters can significantly reduce dimensional deviations.