They improved the quality of parts manufactured with fused deposition modeling by introducing their model. [PERSON] et al. [13] used an artificial neural network in their research to predict the surface roughness of parts printed by fused deposition modeling. They used layer thickness, bed temperature, and printer speed as model parameters. In addition to the optimization of 3D printing, there are also a large number of studies dealing with the application of artificial neural networks in the process of optimizing machining parameters. [PERSON] et al. [14] used an artificial neural network for the face milling of an Al alloy. Parameters such as depth of cut, feed rate per tooth, cutting fluid, engagement of the cutting tool, and cutting forces were used in the model. The obtained results from this study showed that artificial neural networks can be highly accurate. [PERSON] and [PERSON] [15] developed an artificial neural network model to predict surface roughness in the machined surface turning of AISI H13 steel. They assessed the model's predictive capacity and calculated the root mean square error. The model achieved a mean accuracy of up to 95.96% with a smaller training data size. In their study, [PERSON] et al. [16] examined the correlations among cutting parameters during the turning of AISI 1030 steel and their impact on surface roughness. They constructed a model that utilized artificial neural networks (ANNs) to forecast surface roughness. In the area of artificial neural network (ANN) modeling, the SCG training algorithm with nine neurons produced the highest prediction accuracy. [PERSON] et al. [17], in their research, employed an artificial neural network and multiple regression analysis in order to predict and minimize surface roughness in the milling process of an AL-7075 alloy. After the milling process, it was necessary to compare the predicted model with the real Ra measured data. They concluded that the results showedalmost the same values in both cases (measured and predicted). [PERSON] et al. [18] found that factors like feed rate and cutting speed have an important impact on surface roughness in the drilling of AISI 1050 materials. They concluded that second-order regression models are the most suitable method for predicting surface roughness. [PERSON] and [PERSON] [19] used different cutting parameters to measure surface roughness in the turning process of AISI 1040 steel. They used multiple regression and artificial neural networks in order to build predictive models depending on speed, feed rate, and depth of cut. The highest correctness was achieved with the ANN model for surface roughness prediction with multiple regression analysis. [PERSON] et al. [20] proposed a model developed by utilizing MRA and an ANN based on cutting parameters and vibration in the milling process of an Al6061 aluminum alloy. Their results indicated that the ANN consistently outperformed MRA in terms of prediction accuracy across all training data. In a study by [PERSON] et al. [21], a neural network model was developed to predict surface roughness in the milling process of polyamide. The model utilized spindle speed and feed rate as inputs and produced accurate results. [PERSON] et al. [22] developed ANN and MRA models to predict surface roughness in aluminum 6061 alloy milling. The ANN model outperformed the MRA model in accurately predicting surface quality, with a model with two hidden layers achieving a higher accuracy rate (99.27%). [PERSON] et al. [23] determined that, in artificial neural network modeling, the key factors affecting prediction accuracy are the number of neurons and layers in the hidden layers. According to the prediction model, the optimal surface roughness value in the end-milling process of titanium alloys is achieved at a high speed, low feed rate, and radial rake angle. [PERSON] et al. [24] worked with high-purity graphite and created an ANN model in order to predict surface roughness in their research.