## 1 Introduction
This way, a novice user can design a component using a simple isotropic numerical model. The Volume Average Stiffness (VAS) model [19] is based on the Rule of Mixtures (ROM), but it accounts for each region of the cross-sectional area. In addition, it accounts for the infill-type adopting models proposed by [PERSON] and [PERSON] [25] for cellular materials and the composite's anisotropy. Recently, the authors of [15; 26] applied the VAS method, including porosity and the Tsai-Hill equations, to predict mechanical properties. It was acknowledged that developing a numerical model with individual properties (fiber and matrix) takes advanced skills. The model requires a deep understanding of composite theory [27] to be adopted, which may impede the popularization of the printing technique. On the other hand, Machine Learning (ML) has been extensively used and applied in different areas and applications. Therefore, ML could link printing variables and mechanical properties. When focusing on AM, there are plenty of works that make use of data-driven methods to predict material properties [28; 29]. In conventional manufacturing, Artificial Intelligence (AI) has been applied to predict surface roughness, welds, forging, geometric tolerance, sheet metal, and quality control in AM parts [30]. On the other hand, AI systems have the ability to recognize patterns, identify trends within data, and categorize them based on such patterns. Although AI may be exceptionally good at pattern recognition, it has limits, such as sensitivity to biases in training data and its limitation to generalize beyond known patterns. Furthermore, several works have focused on applying ML to predict material's mechanical response [29]. [PERSON] et al. [28] reviewed how authors explored the Artificial Neural Network (ANN) in ML. [PERSON] [31] used an ANN to predict static properties of dual-phase steel from processing and chemical composition. Furthermore, [32] cited several cases where ML was used for strength, stiffness, and strain prediction. Finally, in composites, ML techniques have been used for composites, including the manufacturing method, simulation, and mechanical testing as data input to search for a constitutive model as a whole or a Representative Unit Cell (RUC) [33; 34]. Now, more specifically in AM, ML has been used extensively as well. [PERSON] et al. [35] used ANNs to predict the mechanical behavior of the Fused Deposition Modeling (FDM) of polymer parts accounting for temperature, printing speed, and layer height. [PERSON] et al. [36] used several machine learning algorithms to predict elastic modulus and strength based on selected printing parameters. The study concluded that fiber angle and fiber content are the factors with the most influence on stiffness and strength. However, the \(R^{2}\) for their prediction was as low as 0.19 for a fine tree network and 0.66 for the Matern 5/2 Gaussian Process Regression model, whereas a micromechanic-based model gave a \(R^{2}\) of 0.74. One can see that there is still room for improvement. Modeling mechanical properties with the existing methods is rather complex due to the large number of variables involved (see Figure 2). To use homogenization such as for a representative volume unit and others, one needs advanced numerical skills. This could be a roadblock to popularizing the use of AM composites. Therefore, the present paper attempts to ease that task by predicting mechanical properties using ML methods.