The prediction results were much more accurate with the ANN model than with traditional methods, the experimental design method, and the Taguchi method. [PERSON] et al. [25] used three artificial neural networks (ANNs) to predict surface roughness in the turning process of particulate-reinforced aluminum matrix composites. They used evolutionary algorithms instead of traditional backpropagation, demonstrating that genetic algorithms significantly improved the accuracy of the output model. According to [PERSON] et al. [26], ANNs trained with small data can predict average surface roughness (Ra) in machining processes. [PERSON] et al. [27] investigated the performance of RSM and an ANN in predicting Ra in the machining process of carbon steel SS400. According to their results, the ANN model predicted the Ra more accurately than the RSM model. [PERSON] [28] proposed a novel approach for predicting surface roughness in the CNC machining of aluminum alloys using artificial neural networks, demonstrating a close correlation between the measured and predicted results. [PERSON] et al. [29] conducted a study on utilizing neural networks to predict surface roughness in the CNC machining process of polypropylene. The results showed that an intermediate range of CNC parameters would lead to better surface roughness, and at maximum feed rate, depth of cut, and speed, the average surface roughness was the highest. Limitations in the 3D printing process, such as low roughness and low dimensional accuracy, lead to the need to combine it with traditional manufacturing methods. This concept led to the development of a hybrid manufacturing process (HM) that combines AM and CNC manufacturing. [PERSON] et al. [30] conducted a study on optimal milling parameters for machining FDM-printed parts using ABS and PLA as the primary materials. Their findings indicated that a spindle speed of 3500 rev/min was optimal for the face milling of 3D-printed components. [PERSON], in [31], designed a hybrid manufacturing form able to combine milling and printing operations on the same structure. [PERSON] et al. [32] used MRA to predict optimal values for parameters like layer spindle speed, height, printing speed, material compensation flow, milling depth, and feed speed. They concluded that layer height was the primary factor influencing surface roughness, with minimal roughness achieved at the highest layer height. [PERSON] et al. [33] conducted a study on the combination of computer numerical control (CNC) and 3D printing processes, which is a hybrid technique. Following the milling process in this hybrid approach, the surface roughness was reduced to 4.870-24.511 \(\upmu\)m from 17.332-56.021 \(\upmu\)m.  
From this review of the literature, it can be concluded that previous research has mainly focused on the application of ANNs in process parameter optimization, both in AM and in process parameter optimization in the CNC machining of metal parts. ANN and MRA methods are used to obtain a better prediction of surface roughness. Research in the area of HM has included some investigation into the use of artificial neural networks (ANNs) for modeling and predicting surface roughness in HM after CNC milling. The aim of this study is to represent how milling processing parameters affect the surface quality of 3D-printed components and to create artificial neural network (ANN) models for predicting surface roughness.