## 5 Conclusions  
Predicting mechanical properties for composite materials fabricated via AM is challenging. Currently, the available predictive models do not include all the variables and require highly skilled users with vast knowledge of composite material's mechanical behavior and advanced numerical modeling skills. This work evaluated different supervised learning models in order to check the suitability of employing ML for automatically predicting the mechanical properties of AM composite specimens. The predictive models were trained to estimate the strength and elasticity of AM composite reinforced with fibers like carbon, Kevlar, and fiberglass. To do so, a diverse dataset was constructed based on data published in the literature.  
The results obtained for the ML model, CAT, and the suitability of the proposed modeling workflow for obtaining a predictive model are capable of estimating, with reasonable performance, the mechanical properties of AM composite specimens. The best overall performance was achieved by the CAT and KNN models, with the first standing out since it required less time to estimate the mechanical properties of a given sample. The model takes around 1 ms to make a prediction while achieving relatively low error rates of 9.4446 and 113.1730 for \(E\) and \(\sigma\) prediction, respectively.  
Despite the achievements reported, there is room for improvement in future work. The results are limited to the parameters and materials studied (nylon matrix and carbon, Kevlar, and fiberglass fibers). With this in mind, no applications with cellular structures such as gyroid honeycombs, foams (open- and closed-cell), lattices (grids, octets, face cubic centered, body cubic centered, or X-shaped), and others cannot be considered to be used in conjunction with the results found here. Other parameters, which users can tweak in different systems, such as printing and feeding speed, cannot be included here as the Eiger(r) software does not allow modification. Moreover, although the dataset comprises several samples from different sources, it is still a small dataset from the ML perspective. For this reason, data augmentation techniques can be explored in future work. To improve robustness in prediction, Physics-informed Neural Networks (PINNs) [76] can be used. For this, data could be organized in feature ranges to observe holes within that can be filled through simulation, i.e., with the finite element method. Such numerical data can be fed to an ML algorithm in conjunction with experimental data.  
Conceptualization, J.G.D.; Software, G.R.B.F. and M.G.d.C.R.; Formal analysis, J.G.D.; Investigation, D.P.P. and G.R.B.F.; Resources, A.M.B.B.; Data curation, J.G.D.; Writing--original draft, D.P.P., G.R.B.F., J.G.D. and M.G.d.C.R.; Writing-review and editing, J.G.D. and A.M.B.B.; Visualization, D.P.P. and G.R.B.F.; Supervision, M.G.d.C.R.; Project administration, A.M.B.B.; Funding acquisition, A.M.B.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The raw data supporting the conclusions of this article will be made available by the authors upon a reasonable request. The authors express their gratitude to the Laboratorio do Sensores da Fibra Optica (LSFO) at PUC-Rio. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.