## 6 Conclusions  
In the field of additive manufacturing, FFF stands out as a transformative technology that enables the layer-by-layer construction of components without the constraints of traditional manufacturing methods. Its versatility in material usage, particularly with thermoplastics such as PLA, has attracted attention in the automotive, aerospace, and medical industries. However, the ever-increasing demand for the improved performance of 3D-printed components has spurred the integration of advanced techniques, and this study explores the pivotal role of ML in achieving this goal. The primary objective of this work was to exploit the capabilities of ANN to predict the mechanical performance of FFF-manufactured parts. As part of a comprehensive investigation, experiments were designed using [PERSON]'s parametric approach, with seven key process parameters carefully varied to study their influence on tensile strength. A fractional Taguchi L16 DOE was implemented, and the manufactured parts were tested to assess the mechanical performance of each combination. The ANN showed the ability to predict tensile strength with an R\({}^{2}\) greater than 90%. In addition, the LOO cross-validation approach provided a robust evaluation of the stability and generalization capabilities of the model with the MSE and MAE of 0.002 and 0.024. The ANN model, trained and evaluated on the dataset, demonstrated its effectiveness in predicting unknown values. The low margin of error observed on the subsequent independent test dataset further underscored the reliability of the developed model. The neural network with the highest R\({}^{2}\) value, equal to approximately 97%, was used to check the percentage variation between the predicted and actual values. The best model has been selected according to the score on the training and validation. The variations on the UTS remain stable within a range of \(-1.17\%\) to \(2.56\%\). \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Test**} \\ \cline{2-3}  & \(\mu\) & \(\sigma\) \\ \hline R\({}^{2}\) in \% & 92.48 & 4.90 \\ MAE & 0.070 & 0.017 \\ MSE & 0.006 & 0.002 \\ COP in \% & 99.20 & 2.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Test performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combination** & **UTS in MPa** & **UTS\({}_{\text{pred}}\) in MPa** & **Variation\({}_{\text{UTS}}\) in \%** \\ \hline C1 & 54.945 & 54.325 & \(-1.13\) \\ C2 & 52.671 & 52.054 & \(-1.17\) \\ C3 & 42.554 & 43.643 & 2.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental and predicted data from the test. Percentage variation of each predicted output from the experimental value. In conclusion, this study not only advances our understanding of the intricate dynamics within FFF-manufactured parts but also underscores the transformative potential of ML in optimizing and predicting mechanical performance. As industries increasingly embrace additive manufacturing, the insights gained from this work serve as a beacon to guide the path toward more efficient, reliable, and precisely engineered 3D-printed components. As demonstrated, the fusion of FFF and ML technologies drives toward a future where additive manufacturing is at the forefront of innovation and quality assurance. Future works will include a wider range of combinations and a greater expansion of input ranges to promote a more comprehensive and complete dataset. In this way, it will be possible to increase the representativeness of the database. Conceptualization, V.V. and W.P. ; methodology, V.V. and W.P. ; software, V.V. and M.S.J.W. ; validation: V.V. and W.P. ; formal analysis, V.V. ; investigation, V.V. ; resources, M.S.J.W. ; data curation, V.V. ; writing--original draft preparation, V.V. ; writing--review and editing, V.V., W.P., M.S.J.W. and S.G.; visualization, V.V. ; supervision, W.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.