## 2 Literature Review  
Numerous studies have explored the dimensional accuracy of FDM-printed parts. According to [5], layer thickness has a major influence on precision, with reductions in thickness leading to improved accuracy. Additionally, [6] emphasizes that infill density plays a key role in maintaining dimensional stability, as higher infill percentages help minimize distortions. A critical aspect highlighted in previous research is the impact of part orientation on dimensional accuracy. Studies by [7] have shown that vertical orientation can result in greater deviations due to the accumulation of layer-by-layer errors. Moreover, findings from [8] suggest that optimizing the extrusion path can significantly reduce dimensional variations, enhancing the overall precision of printed components. The FDM process involves complex interactions between various parameters, making it challenging to predict output responses accurately using traditional methods. In recent years, researchers have focused on optimizing one or multiple performance responses in the FDM 3D printing process, employing a diverse array of techniques to enhance dimensional and geometric accuracy. Among non-traditional approaches, artificial neural networks (ANNs) and genetic algorithms (GAs) have gained significant popularity in engineering applications, particularly in optimizing manufacturing processes, due to their ability to model complex relationships and enhance process efficiency [9]. [PERSON] et al. [10] investigated the influence of three key process parameters in the FDM method--layer thickness, raster width, and build orientation--on the mechanical properties of printed parts. They employed the artificial neural network (ANN) technique to predict the compressive and impact strength of the specimens. Their findings demonstrated a strong correlation between experimental results and ANN predictions, validating the reliability of the developed models. [PERSON] [11] conducted an experimental study to analyze the impact of processing variables--processing time, energy consumption of the 3D printer, and dimensional accuracy--on FDM-manufactured parts. Using the Taguchi design, the study optimized five key parameters: layer thickness, filling pattern, printing plane, orientation, and part positioning on the build platform. The results revealed that the printing plane was the most influential factor in minimizing energy consumption and reducing processing time. [PERSON] et al. [12] employed a hybrid approach combining genetic algorithms (GAs), artificial neural networks (ANNs), and rational functions to determine the optimal set of printing parameters--layer thickness, infill density, and imposed clearance between the shaft and the hole--for optimizing the absolute relative clearance of cylindrical 3D-printed parts. While the study provided valuable insights into dimensional accuracy, aspects related to operational efficiency, such as energy consumption, scrap weight, and surface roughness, were not within its scope. [PERSON] et al. [13; 14; 15] analyzed the impact of process parameters on the dimensional accuracy of FDM-printed parts using the grey-Taguchi method and artificial neural networks (ANNs). Their findings indicated that an optimal combination of process parameters--including a layer thickness of 0.254 mm, a part orientation of 0\({}^{\circ}\), a raster angle of 0\({}^{\circ}\), a raster width of 0.4564 mm, and an air gap of 0.008 mm--yields the best accuracy. Additionally, their investigation into mechanical properties identified the raster angle as the most influential factor [16]. To optimize sliding wear and surface finish, they applied response surface methodology, achieving improved results [17]. [PERSON] et al. investigated the sliding wear performance and dimensional precision of FDM-printed parts using a Taguchi-based artificial neural network (ANN) approach, identifying part orientation as the most influential factor [18]. Similarly, [PERSON] et al.