Modeling and Prediction of Surface Roughness in Hybrid Manufacturing-Milling after FDM Using Artificial Neural Networks  
[PERSON]  
1 Kosovo and Metohija Academy of Applied Science, 38218 Leposavic, Serbia; [EMAIL_ADDRESS]. (S.D. ); [EMAIL_ADDRESS]. (M.M. ); [EMAIL_ADDRESS]. (B.S. ); [EMAIL_ADDRESS] (M.F.) 1  
[PERSON]  
2 Faculty of Technical Sciences, University of Pristine, 3820 Kosovska Mitrovica, Serbia; [EMAIL_ADDRESS] (D.L. ); [EMAIL_ADDRESS] (B.C.) 2  
[PERSON]  
2 Faculty of Technical Sciences, University of Pristine, 3820 Kosovska Mitrovica, Serbia; [EMAIL_ADDRESS] (D.L. ); [EMAIL_ADDRESS] (B.C.) 2  
[PERSON]  
1 Kosovo and Metohija Academy of Applied Science, 38218 Leposavic, Serbia; [EMAIL_ADDRESS]. (S.D. ); [EMAIL_ADDRESS]. (M.M. ); [EMAIL_ADDRESS] (B.S. ); [EMAIL_ADDRESS] (M.F.) 1  
[PERSON]  
3 Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia; [EMAIL_ADDRESS] (B.C.) 3  
[PERSON]  
1 Kosovo and Metohija Academy of Applied Science, 38218 Leposavic, Serbia; [EMAIL_ADDRESS]. (S.D. ); [EMAIL_ADDRESS]. (M.M. ); [EMAIL_ADDRESS] (B.S. ); [EMAIL_ADDRESS] (M.F.) 1  
[PERSON]  
1 Kosovo and Metohija Academy of Applied Science, 38218 Leposavic, Serbia; [EMAIL_ADDRESS]. (S.D. ); [EMAIL_ADDRESS]. (M.M. ); [EMAIL_ADDRESS] (B.S. ); [EMAIL_ADDRESS] (M.F.) 1  
[PERSON]  
2 Faculty of Technical Sciences, University of Pristine, 3820 Kosovska Mitrovica, Serbia; [EMAIL_ADDRESS] (D.L. ); [EMAIL_ADDRESS] (B.C.) 2  
the cutting operation can be set up in advance. However, factors such as tool geometry, tool wear, and chip formation must be monitored, or the material properties of both the part and the tool will be uncontrolled [4]. In order to improve the machining process and reduce manufacturing time and costs, the industry is increasingly turning to modeling techniques [5]. Artificial neural networks (ANNs) are machine learning algorithms that are highly effective, and they take inspiration from the human brain's functionality and complex structure. ANNs consist of interconnected neurons, which make it possible for them to transmit and process information with great efficiency. ANNs are useful in various applications, such as prediction, pattern recognition, image and speech recognition, and classification [6]. Several researchers have investigated the impact of 3D printing parameters on surface quality. Models have been developed to predict surface roughness using artificial neural networks and multiple regression analysis. [PERSON] et al. [7], in their study, machined polypropylene parts and concluded that average surface roughness values of 0.599 \(\upmu\)m can be obtained with the parameters of 1241 mm/min feed rate, 4138 rpm speed, and 0.5 mm depth. [PERSON] et al. [8] concluded that decreasing the printing speed, outer shell speed, layer thickness, and print temperature, which are FDM input parameters for ANNs, resulted in 12.36% higher accuracy than the RSM method, and minimum surface roughness was approximately 2.011 \(\upmu\)m. In a study conducted by [PERSON] et al. [9], an artificial neural network was employed to forecast the surface roughness of Acrylonitrile Butadiene Styrene (ABS) parts. The study investigated the influence of varying numbers of neurons and activation functions. Using an artificial neural network and feed-forward backpropagation, [PERSON] et al. [10], in their study, discovered that layer thickness greatly affects the surface roughness of PLA components, whereas feed rate has no significant effect on it. [PERSON] et al. [11] developed models for surface roughness prediction in fused deposition modeling, considering extruder temperature, layer thickness, and infill density. They used artificial neural networks, support vector machine regression, and regression analysis models. The study found that the artificial neural networks produced better results than the SVR and multivariate linear regression models. [PERSON] et al. [12] introduced an artificial neural network that combined dimensional analysis conceptual modeling and a classic ANN.