## 1 Introduction
In order to alter the laser beam profile or spot size, many techniques have been implemented, including ring mode or laser defocusing [19; 20; 21; 22], as well as the utilization of a multilayer machine [23]. Thus, a model that allows for the prediction of melt pool morphology and formation is significantly needed to speed up and optimize the identification of the printability map in the L-PBF process. The possibility of obtaining preliminary information on melt pool shapes as a function of the laser power and scanning speed combination without any printing could significantly reduce the number of physical experiments, thereby strongly reducing the number of microstructural analyses on the material, which are expensive and time-consuming. Moreover, the model could improve the choice of the best laser power and scanning speed, allowing for a very accurate investigation of the P-V space. This work aims to use a supervised machine learning (ML) model for regression to identify the best model able to synthesize the experimental data on melt pool shapes. The application of ML models to predict several phenomena in the L-PBF process has been carried out by researchers in the last few years [23; 24; 25]. For instance, in previous literary studies, the employment of ML models allowed for the evaluation of the keyhole and balling phenomena [13], which are difficult to predict using the conventional FEA methods. Moreover, the surrogate model [26] enabled the prediction of melt pool geometry as a function of the laser power, scanning speed, and beam spot size for a 316 L stainless steel case study. In addition, some researchers [27; 28; 29; 30; 31; 32] have implemented machine learning-based monitoring of the L-PBF process based on in situ laser single-track videos, allowingfor the possibility to predict track widths with a high accuracy (R\({}^{2}\) of 0.93). In [30], the goodness of several machine learning models were evaluated to predict the melt pool behavior on a wide range of metal alloys and consider different AM processes, such as L-PBF and direct energy deposition (DED). Despite all these promising results, the effect of some parameters, such as the layer thickness and building platform temperature, needs to be investigated more deeply. The layer thickness turns out to be one of the most important parameters to be increased to enhance the low productivity of this process [2,33] and to optimize the application of some printing strategies such as hull bulk [34]. Thus, the possibility to predict the melt pool formation and consequently the identification of the printability window (usually narrower as the thickness increases [35]) for high layer thickness parameters through an ML model results fundamentally in matching the major L-PBF needs. In addition, the effect of the preheating temperature is a very important parameter to be analyzed and investigated [36, 37, 38, 39]. This parameter's results are necessary to reduce part distortions and material residual stresses, minimize heat loss, and improve adhesion between the first deposed powder layers between the part and the building platform for melt pool formation. Thus, this work aims to evaluate, using KNIME Analytic Software Version 4.7.7 through the AutoML Regression tool, the most appropriate model able to synthesize the experimental data on melt pool shapes. Moreover, in order to provide an accurate and large enough dataset to properly train the ML models evaluated in this case study, numerous experimental campaigns on Inconel 718 alloy specimens were carried out, considering the effects of the variations in laser power, scanning speed, layer thickness, and building platform temperature. The structure of this paper is organized as follows: Section 2 describes the experimental test settings and the models evaluated and analyzed using the AutoML Regression tool. Section 3 shows the results achieved for each case study in terms of physical results, predicted results as a function of the regression model evaluated (by AutoML), and Rosenthal solution results.