## 4 Conclusions  
Six algorithm models, ANN, ANFIS, ANN-PSO, ANN-GA, ANFIS-PSO, and ANFIS-GA, have been employed to predict the surface roughness of blocks of aluminum Al6061 machined on a CNC milling machine. Specific parameters, namely the spindle speed of rotation, the feed rate, and the axial and the radial depth of cut, have been used as data inputs to train the models, while the target output was the surface roughness of the machined aluminum. Parametric analysis has been performed to analyze how the hyperparameters of each model affect the prediction performance. This study reveals that an adjustment of the configuration of the models alters their robustness. Though most models exhibit a relatively higher memorization capability, as suggested by the values of the training regression, the ANN models would potentially yield the best results, based on the testing regression value. The study has shown that the effectiveness of the model being trained is heavily influenced by the hyperparameters. It highlights the impact on the quality of the model being trained. These parameters are responsible for regulating the behavior of the model and have a direct impact on its performance. The study has demonstrated that the correct selection of hyperparameters can significantly improve the overall accuracy and efficiency of the model. Thus, it is crucial for researchers and data scientists to carefully consider the hyperparameters during the training processes of models to ensure the best possible results. This study demonstrates that although soft computing techniques could potentially enhance the Al6061's surface roughness, the robustness of the models is closely related to their hyperparameters, which cannot be estimated a priori in many practical cases. The results reported in this study show that data could be used to build robust machine learning models. The robustness of these models could be enhanced through the optimization of the hyperparameters affecting their prediction performance. To strengthen the present study, our next goal is to use the predicted data results in a randomized manner during an experiment on the CNC milling machine. By doing so, we can draw a comparison between the results obtained from the predicted data and the actual experiment, which will enable us to validate the efficacy of our model. This step is crucial in ensuring that the predictions made by our model are accurate and can be relied upon for future experiments. It is our constant endeavor to improve the accuracy of our models and ensure that the results obtained are reliable and trustworthy. Through this exercise, a deeper understanding of the CNC milling machine and its behavior can be gained, which will contribute to the advancement of the manufacturing engineering field.  
Figure 22: ANFIS-GA deviation graph.  
**Author Contributions:** Conceptualization, S.B. and L.K.T.; methodology, S.B., I.P.O. and L.K.T.; software, S.B.; validation, I.P.O. and L.K.T.; formal analysis, S.B.; investigation, S.B.; resources, L.K.T.; data curation, I.P.O.; writing--original draft preparation, S.B.; writing--review and editing, L.K.T. and I.P.O.; visualization, S.B.; supervision, L.K.T. and I.P.O.; project administration, L.K.T.; funding acquisition, L.K.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data have been included in the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.