# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
## 6 Conclusions  
This study aims to evaluate different configurations of an ANFIS model to enhance the prediction of the mechanical properties of AM specimens by refining key model parameters. By integrating the adaptive learning capabilities of neural networks with the interpretability of fuzzy logic, this research addresses critical challenges associated with modeling the complex relationships inherent in additive manufacturing (AM) processes. These challenges include variations in layer thickness, infill pattern, printing speed, and material characteristics. The developed ANFIS model provides a robust and adaptive framework for predicting and enhancing the mechanical performance of AM-produced parts, as the prediction of mechanical properties of AM parts is a complex multifactorial problem. The research highlights several key innovations. First, the implementation of ANFIS enables the adaptive refinement of printing parameters based on experimental data, significantly enhancing the accuracy of predictions for critical mechanical properties such as stress, strain, and Young's modulus. This capability not only enhances the quality and consistency of printed parts but also reduces the need for extensive physical testing and iterative prototyping, saving both time and resources. Furthermore, the study investigates two fuzzy partitioning techniques--grid partitioning and subtractive clustering--demonstrating the flexibility of ANFIS in capturing complex input-output relationships. Grid partitioning provided a structured framework for rule generation, while subtractive clustering significantly improved efficiency by identifying effective rule sets, even when handling noisy data. Across all three cases examined, subtractive clustering demonstrated high effectiveness, achieving an improved predictive accuracy for mechanical properties such as stress, strain, and [PERSON]'s modulus. For stress, the SC model produced accurate predictions that closely aligned with actual values, particularly for moderate stress levels, while minimizing deviations observed in extreme cases. In the case of strain predictions, the SC model consistently delivered estimates closer to the actual strain values, reducing the larger deviations observed in other configurations. Similarly, for [PERSON]'s modulus, the SC model provided the most accurate results, especially at lower values and near-zero actual values, outperforming other configurations that exhibited a tendency to overestimate. Despite the computational challenges posed by the high dimensionality of input data in grid partitioning, the study successfully addressed these limitations by configuring the model to use two membership functions of the same type for each input. This adjustment ensured efficient processing without compromising accuracy, even in the absence of advanced computational resources. Overall, the final and the most suitable model has a mean obtained estimation error of 14.13%. These findings underscore the potential of ANFIS as a powerful, adaptive tool for additive manufacturing, capable of dynamically adjusting key parameters and efficiently handling complex data relationships. By leveraging subtractive clustering and dynamic parameter refinement, this study presents a scalable, data-driven framework that enhances the mechanical reliability of 3D-printed components, offering significant value across diverse manufacturing environments. Future research could be focused on expanding the dataset with more diverse materials and advanced AM technologies to enhance the model's predictive accuracy and applicability. Incorporating real-time sensor data during the printing process could enable the development of a closed-loop feedback system, where the ANFIS model could dynamically adjust parameters to correct deviations and improve print quality on-the-fly. Additionally, future work may involve expanding the application of ANFIS and related machine learning technologies in AM by integrating real-time data and developing more sophisticated predictive models.