## 5 Conclusions and Future Perspectives  
Artificial intelligence can be helpful in several areas of activity. We presented a model that uses ANNs and a genetic algorithm (neuroevolutionary approach) to estimate the tensile strength of manufactured parts made by 3D printing. Therefore, for this paper, we also generated a database from experimental literature that investigated the effect of process parameters and the result obtained for the tensile strength property of the material. Then, an ANN model was developed for the own database, which predicts the tensile strength as a function of the printing process parameters. The developed model can be helpful in structural and economic optimizations of parts that perform an engineering function.  
The execution plan of the neuroevolutionary model showed a difference between the values of \(R^{2}\), which happens due to the random processes of the genetic algorithm, such as the generation of the initial population, selection, crossover and mutation. We verified that applying a population metaheuristic with genetic algorithms leads to potential gains in learning a neural network and confers an outstanding speed in the training mechanism. The idea of feedforward and backpropagation can be pretty compelling, assuming that they minimize the quadratic error. However, performing the training function for specific applications takes a long time.  
Figure 4: Comparison chart of the performance of variations.  
In future research, we intend to replace the evolutionary aspect of our learning model with one that tends to converge more quickly. In our planning, we will apply the training method using a variety of genetic algorithms known as biased random-key genetic algorithms [35; 36]. The primary purpose is to consider larger populations to achieve higher learning rates, assuming that the convergence time of the solutions tends to be smaller. Thus, we can further diversify the execution parameters of the evolutionary algorithm by establishing similar execution time limits to define the model.  
Conceptualization, R.R.B.M.; data curation, M.A.d.S. and R.R.B.M.; methodology, B.A.J. and P.R.P.; software, M.A.d.S.; validation, B.A.J.; writing--original draft, B.A.J. and P.R.P.; writing--review and editing, B.A.J. and M.A.d.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. These data can be found here: [[https://github.com/matheusalencar23/tcc](https://github.com/matheusalencar23/tcc)]([https://github.com/matheusalencar23/tcc](https://github.com/matheusalencar23/tcc)), accessed on 17 May 2022. The README.md file talks a little about the repository. The \"data.csv\" file contains the data used for training. The \"main.py\" file contains the algorithm responsible for the neural network optimization tests based on the genetic algorithm. The \"test.py\" file contains the algorithm used to generate the loss curve graphs. The \"af.py\" file is responsible for the final implementation of the optimized network. The \"helpers.py\" file contains functions used in different situations. The \"images\" folder and the files \"tests.txt\", \"times.txt\" and \"data_table.csv\" contain the results of the optimizations. The results are separated into \"v1\", \"v2\" and \"v3\" folders based on the run settings.  
[PERSON] is grateful to the University of Fortaleza/Edson Queiroz Foundation and to the National Council for Scientific and Technological Development (CNPq) for developing this project.  
The authors declare no conflict of interest.