## 1 Introduction
Figure 1: Surface roughness profile: yi is the vertical distance from the mean line to a given data point along the profile line; l is the mean width of the profile line. Although empirical and analytical techniques are suitable for the modeling and the analysis of the surface roughness, they have the following limitations [8; 9]:  
* Empirical and analytical approaches are time-consuming, especially when a large amount of data is required for analysis;
* Because the amount of data used for the analysis is significant, building models using these traditional approaches is relatively more challenging;
* Because there are so many variables to consider, the experiment is susceptible to failure to generate the expected findings. ANN is a computing system composed of a collection of connected nodes emulating neurons in the human brain and capable of solving problems such as function approximation, classification, and time-series prediction. The ANN approach has lately gained researchers' interest in addressing and modeling complicated issues and dealing with nonlinearity between parameters in various fields. It is a multilayer network in which every neuron in one layer is fully linked to every neuron in the layer above it. Figure 2 depicts a typical ANN arrangement. Various researchers have used the ANN approach to predict the surface roughness of machined components: [PERSON] and [PERSON] [10] investigated the impact of machining parameters on the surface roughness of a high-strength carbon fiber composite plate using ANN. The analysis showed that the cutting tool and the feed rate have a stronger impact on the surface roughness than the spindle speed. As for ANN settings, the authors chose feedforward backpropagation as the model architecture because of its popular application. [PERSON] et al. [11] used the ANN approach to analyze the turning operation and predict the surface roughness of an Inconel 718. Data were obtained from the machining of the Inconel 718 using cryogenically treated and untreated inserts. The surface roughness was predicted with 98% accuracy. According to the study, the ANN model is more reliable than the regression-based models in predicting Inconel 718's surface roughness in the turning operation. The ANN parameters can be altered to allow better combinations for the model's improvement. [PERSON] [12] used uncoated carbide composites to produce AA7039/Al2O3 metal matrix composites and examined the impact of milling settings on the surface roughness and cutting force using a neural network and the Taguchi method. The Levenberg-Marquardt and the backpropagation algorithms were used to achieve better results. Furthermore, [PERSON] and [PERSON]. [13] pointed out that the Levenberg-Marquardt method, which converges more frequently and speeds up training, is acknowledged as having  
Figure 2: Artificial neural network structure. substantially superior performance than backpropagation, which is likely the most widely used ANN algorithm. [PERSON] and [PERSON] [14] compared the performance of ANN and ANFIS in predicting the machining response (metal removal and tool wear). They conducted hyperparametric evaluation by modifying learning algorithms (Levenberg-Marquardt, scalar conjugate gradient, and resilient backpropagation) and activation functions (logsig, tangsig, and purelin), and a range of neurons (2-20) were assessed to find the most effective one to perform training, validation, and testing. According to [PERSON] [15], the complexity of the system being modeled determines how many neurons are needed in the hidden layer. [PERSON] et al. [16] predicted and optimized the surface roughness and cutting forces in the slot milling of aluminum alloy 7075-T6 using ANN and a multi-objective genetic algorithm. Machining parameters included the cutting speed, feed per tooth, depth of cut, and tool type. Hyperparameters were fine-tuned and the Levenberg-Marquardt and RMSprop algorithms were chosen to train the model.