## 5 Conclusions  
This study presents a comprehensive analysis of dimensional deviations in FDM-printed splined shafts and hubs, highlighting the key parameters influencing accuracy. The main conclusions drawn from the research are as follows:  
Printing parameters significantly affect dimensional accuracy--layer thickness, infill density, and nominal diameter contribute to deviations, with lower layer thickness generally improving precision.  
Artificial neural networks (ANNs) provide reliable predictions--the ANN models trained in this study achieved high accuracy, with correlation coefficients above 0.91, proving their effectiveness in predicting dimensional deviations.  
Genetic algorithm (GA) optimization enhances accuracy--multi-objective optimization allowed for the identification of the best printing conditions, reducing deviations and improving manufacturing consistency.  
Measurement and validation confirm model reliability--CMM-based dimensional measurements and statistical validation confirmed that optimized printing conditions yield improved precision.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measured Values** & **ard\_Shaft** & **ard\_Hub** \\ \hline \(\mathcal{O}\)d1n = 50.12; D1n = 49.91 & \(\text{ard\_O}\)d1e = 0.0024 & \(\text{ard\_O}\)d1e = 0.0018 \\ \(\mathcal{O}\)d2n = 42.06. D2n = 42.07 & \(\text{ard\_O}\)d2e = 0.0014 & \(\text{ard\_O}\)d2e = 0.0017 \\ \(\ln\) = 10.04. Ln = 9.95 & \(\text{ard\_le}\) = 0.004 & \(\text{ard\_Le}\) = 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The measured value for the samples and the values for absolute relative deviations.  
Future work should focus on extending the methodology to other materials and exploring additional post-processing techniques to further reduce dimensional variations.  
This study demonstrates that combining experimental analysis with ANN and GA optimization provides a powerful approach for improving the dimensional accuracy of FDM-printed mechanical components, ensuring better performance in real-world applications.  
We acknowledge that the optimized spline configuration obtained through our study deviates from the standardized combinations defined in ISO 14. This deviation arises as a result of the optimization process, which aims to minimize dimensional deviations in FDM-printed spline shafts and hubs rather than adhere strictly to existing standards.  
It is important to emphasize that our research does not seek to propose an alternative to ISO 14 but rather to demonstrate how process optimization in additive manufacturing can impact dimensional accuracy. The methodology presented may be particularly useful in non-standard applications where flexibility in spline design is acceptable, such as prototyping, lightweight structures, or cases where conventional manufacturing constraints do not apply.  
A.-D.R. and D.-C.A., conceptualization; A.-D.R., D.-C.A. and C.-F.B., methodology; D.-C.A. and T.G., software and 3D models; A.-D.R., D.-C.A., A.S. and C.-F.B., data analysis and writing the paper; D.-C.A., review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available within the article.  
The authors declare no conflicts of interest.