## 3 Materials and Methods
### Machine Learning Pipeline  
The supervised learning-based modeling workflow proposed in this work is summarized in Figure 6. As previously mentioned in Section 3.1, eight variables corresponding to \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Reference** & **Fiber Type** & **Fill** & **Fiber** & **Fiber** & **V\({}_{f}\)** & **Properties** \\  & & **Pattern** & **Angle (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** & **Distrib.** & & \\ \hline Blok [44] & C & T & \(\pm\)45 & 0,45 & NA & X & \(E_{1,}\
u_{1},E_{2},\
u_{3},E_{4},\
u_{5},E_{6},\
u_{12}\) \\ [PERSON] [18] & C & NA & 45 & 0,90\(\pm\)45 & NA & X & \(E_{1,}\
u_{1},E_{2},\
u_{3},E_{4},\
u_{5},E_{6},\
u_{12}\) \\ [PERSON] [45] & C & NA & 0 & NA & C & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [19] & Kv & NA & \(\pm\)45 & 0 & NA & NA & \(E_{1,}\
u_{1},\
u_{1}\) \\ [PERSON] [46] & C, Kv, FG & NA & NA & NA & C & NA & \(E_{1,}\
u_{1},E_{1}\
u_{1}\)/\(\
u_{1}\) \\ [PERSON] [47] & C, FG & NA & \(\pm\)45 & 0,90 & NA & NA & \(E_{1,}\
u,E_{1,}\
u_{1},\
u_{1}\) \\ [PERSON] [48] & C, Kv, FG & NA & \(\pm\)45 & 0,90 & C & NA & \(E_{1,}\
u,\) \\ [PERSON] [13] & C, FG & NA & NA & 0,90 & 1 & [PERSON] & \(J,E_{1,}\
u_{1},\
u_{1}\) \\ [PERSON] [49, 50] & C, Kv, FG & NA & NA & NA & NA & \(E_{1,}\
u_{1},\
u_{1},\
u_{1}\) \\ [PERSON] [51] & C & NA & NA & 0,90\(\pm\)45 & NA & NA & \(E_{1,}\
u_{1},\
u_{1},\
u\) \\ [PERSON] [52] & Kv & R, H & NA & 0,90\(\pm\)45 & LC & NA & \(E_{1,}\
u_{1},\
u_{1}\)/\(\
u_{1}\) \\ [PERSON] [53] & C, FG & T & 0 & 0,90\(\pm\)45 & C & NA & \(E_{1,}\
u_{1},\
u_{1}\) \\ [PERSON] [54] & C, FG, Kv & T & NA & 0 & I & X & \(\
u_{1,}\
u_{1},E_{1}\)/\(\
u_{1}\) \\ [PERSON] [55] & C & NA & NA & 0,90 & NA & NA & \(E_{1,}\
u_{1},\
u_{1}\) \\ [PERSON] [56] & FG & H & NA & 0,90\(\pm\)45 & NA & NA & \(\
u_{1,}E_{1,}\
u_{1}\) \\ [PERSON] [57] & C, FG, Kv, T, H, R & NA & 0 & C & NA & \(\
u_{1,}E_{1}\) \\ [PERSON] [58] & C & T & NA & NA & NA & - & \(E_{1,}\
u_{1}\) \\ [PERSON] [59] & C & NA & NA & 0 & I & NA & \(\
u_{1,}E_{1}\) \\ [PERSON] [60] & C & S & 0 & \(\pm\)45 & I & X & \(\
u_{1,}\
u_{1}\) \\ [PERSON] [61] & C, Kv, HSHT & S & \(\pm\)45 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [62] & C, FG & S & 0 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [63] & C, FG & S & 0 & 1 & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [64] & C & S & 0 & 0,90\(\pm\)45 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [64] & C, Kv & S & 0 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [65] & C & S & 0 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [66] & Kv & S & 0 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [4] & C & S & 0 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [67] & C & R & 0 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [68] & C & R & 0 & \(\pm\)45 & C & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [69] & C, FG & S & 0 & \(\pm\)45 & C & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [70] & Kv & R & 0 & \(\pm\)45 & C & X & \(E_{1,}\
u_{1}\) \\ [PERSON] [71] & C & S & 0 & 0 & I & X & \(E_{1,}\
u_{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset table representing groups of samples retrieved from several published works. The table shows the variations in the values of each feature used in this work. the printing properties defined during the AM process were used as features. These input variables correspond to a mix of numerical and categorical variables, and thus a preprocessing step was required. During the preprocessing stage, operations like feature scaling (numerical features) and encoding (categorical features) were performed to make the data adequate for the predictive models. As shown in Figure 6, eight regression algorithms were considered as candidates to serve as mechanical properties predictors. Five of these algorithms were selected based on a preliminary cross-validation analysis conducted with the help of PyCaret [72]. This low-code Python library makes automatic ML workflows and is suitable for identifying potential models to perform the task at hand. According to the rank established in the preliminary analysis, BAY, CAT, KNN, LAS, and RDG were the most promising models.