## 1 Introduction
The results showed that a significant influence of the short carbon fibers on the strength and heat deflection temperature of the parts was found in PA6. It was also shown that the strength and tensile modulus of the PA6 reinforced with short carbon fibers in the longitudinal direction of the material are up to 39% higher than for the transverse direction. [PERSON] et al. [18] investigated the effect of the orientation of reinforcing fibers in polymer-based composites on the mechanical properties of FDM-printed parts. The results showed that the best strength and stiffness were achieved when the fibers and the layer interfaces were parallel to the sample axis, while these properties were about 60% lower when measured in the direction perpendicular to the fibers. [PERSON] et al. [19] investigated the effects of the FDM process parameters on the tensile properties of carbon fiber-reinforced plastic. The results revealed that samples with 0\({}^{\circ}\) and 90\({}^{\circ}\) raster angles exhibited higher Young's modulus, yield and tensile strengths, whereas samples with raster angles of \(-45^{\circ}\) and 45\({}^{\circ}\) exhibited higher toughness and ductility. The study optimized the process parameters of research examples to specify the infill speed of 25 mm/s, and layer thickness of 0.15 mm for high tensile and yield strengths, whereas a 0.25 mm layer thickness was specified to achieve high toughness and ductility. [PERSON] et al. [20] investigated the effect of process parameters (melting temperature, fiber content, hatch spacing and layer thickness) and the performance of 3D-printed continuous carbon fiber reinforced PLA composites. The results showed that the mechanical properties improve with increasing the melting temperature, but the surface accuracy is affected by increasing the temperature above 240 \({}^{\circ}\)C.  
The applications of various optimization and modeling techniques were adopted in FDM, such as the Taguchi method, the response surface method (RSM), grey relational, fractional factorial, artificial neural networks (ANNs), genetic algorithm (GA) and fuzzy logic. These methods give good and reliable results and offer improvement of the quality characteristics of parts made by FDM technology [21; 22]. Ref. [23] demonstrated the use of RSM for the modeling and optimization of the FDM process parameters. The authors of this paper identified the influence of four important process parameters: thickness of layer, infill density, printing temperature and wall thickness, on the tensile strength of test specimens printed from PLA material. [PERSON] et al. [24] presented an investigation on the effect of three process parameters on the flexural strength of the FDM Nylon 12 fabricated parts. RSM was used to analyze the results. To test the significance of the parameters, the analysis of variance (ANOVA) method was used. The results showed that the raster angle and air gap have a significant effect on the flexural strength. [PERSON] et al. [25] developed a statistical model using the RSM method integrated with GA to predict the optimal FDM process parameters. According to the central composite design (CCD), 86 experiments were performed, considering 6 process parameters, such as air gap, slice height, raster angle, raster width, contour width and orientation. Artificial intelligence plays an essential role in the research and development of manufacturing processes. It became an integral part of optimizing process parameters and in predicting output values based on inputs. The use of artificial neural networks as one of the machine learning methods has also proven to be useful for the modeling of the FDM process. [PERSON] et al. [26] studied the effect of process parameters on the mechanical properties of FDM printed parts made of different materials, where infill density and extrusion temperature were optimized to increase the tensile strength of the FDM fabricated units. ANN combined with GA into a hybrid tool (GA ANN) was used for model generation and optimization purposes. [PERSON] et al.