# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
## 1 Introduction
Notably, the ANFIS model achieves high prediction accuracy by integrating artificial neural networks with fuzzy inference systems, demonstrating reliability across different fiber compositions. This approach offers significant advancements in technical evaluation, supporting broader applications of carbon fiber composites in industries where high predictability and mechanical resilience are essential. [PERSON] et al. [22] developed a Takagi-Sugeno (T-S) fuzzy neural network to predict and optimize the tensile strength of polylactic acid (PLA) components fabricated using fused deposition modeling (FDM) 3D printing. This model incorporated four critical printing parameters--layer thickness, printing speed, temperature, and fill density--to enhance tensile performance. Experimental validation demonstrated the T-S model's accuracy, with relative errors between the predicted and actual tensile strengths ranging from 5% to 10%. The results highlighted the model's capacity to capture the nonlinear relationships within AM processes, providing an efficient method for optimizing tensile strength. [PERSON] et al. [15] developed an ANFIS model aimed at predicting the mechanical properties--specifically, maximum stress, strain, and Young's modulus--of PLA specimens produced through fused filament fabrication (FFF). The study utilized experimental data from three-point bending tests, incorporating various manufacturing parameters such as infill pattern, layer thickness, and extrusion temperature to train the model. This approach enabled accurate mapping between inputs and output mechanical properties, validating the model's effectiveness in predicting material behavior in additive manufacturing (AM) contexts. Analytical models, such as those based on the foundational work of [23], provide a theoretical framework that is invaluable for guiding initial design decisions. However, analytical and numerical methods can face limitations when addressing the complex, nonlinear interactions inherent in AM processes [24]. Such limitations arise due to the anisotropic properties of printed parts and the multitude of interdependent variables, including layer thickness, infill density, material composition, and printing parameters. [PERSON] et al. [24] also highlighted that while analytical methods are computationally efficient, they may not fully account for real-world deviations in AM components. Their study demonstrated that nonlinear machine learning models, despite requiring higher computational resources, performed better in predicting mechanical properties. ANFIS presents an innovative and practical solution for addressing the complexities of additive manufacturing (AM). By combining the adaptive learning capabilities of neural networks with the interpretability of fuzzy logic, ANFIS effectively models the nonlinear relationships between AM parameters and mechanical properties. This study builds on this foundation by expanding the application of ANFIS to a broader range of AM technologies and materials, enhancing its predictive scope and adaptability. The primary aim is to integrate ANFIS with AM processes to develop a robust model capable of predicting critical mechanical properties--such as stress, strain, and Young's modulus--of 3D-printed components. The research advances existing methodologies by addressing the complex, nonlinear interactions inherent in AM processes. The key contributions of this work include the following:  
* Integration of ANFIS with AM Processes: The study applies ANFIS to predict critical mechanical properties--such as stress, strain, and Young's modulus--by dynamically adapting to diverse AM configurations and material properties. * Comparison of Fuzzy Partitioning Techniques: The research evaluates grid partitioning and subtractive clustering to identify the most effective technique for capturing input-output relationships, with subtractive clustering demonstrating superior performance.