## 1 Introduction
The ANN model was generated by training data obtained by experimentations, according to the Taguchi L18 plan, where straight cuts on aluminum alloy 5083 were produced. Two additional data sets were applied for the validation and testing of the developed kerf width model. The mean squared error and correlation coefficients proved the prediction accuracy of the developed model in all three data sets. Consequently, 2D and 3D response surface plots that visualize the process parameters' effects on the analyzed response were generated. Also, these plots served for the determination of optimal process parameter settings that lead to a minimal kerf width. [PERSON] et al. [15] examined the effect of the cutting speed, arc current, and cutting height on the formation of the cross height, molten metal localized on the bottom of metal sheet. In this paper, the authors performed experimental trials, straight cuts, according to the Taguchi L27 design on aluminum alloy 5083. In order to define the relations between the input parameters and cross height quality characteristics, the fuzzy logic technique was applied and a set of fuzzy IF-THEN rules was established. The fuzzy logic model was checked by a comparison between the experimental and predicted response data on a validation experimental data set and with verification measures: the mean absolute percentage error and coefficient of determination. Finally, response surface plots as well as contour plots present process parameter effects and enable the determination of optimal process conditions with minimal cross generation. [PERSON] et al. [16] analyzed the heat-affected zone (HAZ) width when cutting aluminum alloy 6061. The process parameters whose influence was analyzed in this paper are the arc current, standoff gap, and gas pressure. Additionally, a regression mathematical model of the heat-affected zone was developed. This model was optimized by a partial swarm optimization algorithm and corresponding process parameter levels that result with a minimal HAZ were identified. [PERSON] et al. [17] conducted experimentations according to the Taguchi L9 experimental plan by varying the levels of process parameters: the arc current, feed rate, gas pressure, and cutting distance when cutting aluminum alloy 5083. They performed multi-objective optimization by GRA in order to define the process parameter values that lead to minimal surface roughness and conicity responses. Additionally, ANOVA was applied to estimate the significance of each parameter on a grey relational grade. [PERSON] et al. [18] investigated the influence of plasma jet cutting process conditions on responses such as the material removal rate, top and bottom kerf width, and level angle in cutting aluminum alloy 6082. The process parameters that were taken into account were the arc current, standoff distance, gas pressure, and cutting speed. To determine the process parameter settings that result in optimal cut quality characteristics, the authors generated main effects plots. Additionally, ANOVA was performed to discuss the statistical significance of each parameter on the analyzed responses. As it is already seen, regarding the cut quality characteristics, the authors examined the cut quality of straight cuts through responses such as: the surface roughness, kerf width, taper, level angle, material removal rate, drops height, heat-affected zone, etc. Additionally, several studies [19; 20; 21] have focused on circular cuts and their quality characteristics, due to the distinct behavior of the plasma jet observed during rounded cutting. [PERSON] et al. [19] developed the ANN model that predicts the level angle circular cut quality response independent of the plate thickness, cutting speed, arc ampere, arc voltage, air pressure, pierce height, and torch standoff distance in cutting an St37 carbon steel plate. Experimental trials were conducted according to the Taguchi L18 design. These data were divided into three data sets: for training the ANN model, its validation, and testing.