Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process.

This paper explores the integration of adaptive neuro-fuzzy inference systems (ANFIS) with additive manufacturing (AM) to enhance the prediction of mechanical properties in 3D-printed components. Despite AM’s versatility in producing complex geometries, achieving consistent mechanical performance remains challenging due to various process parameters and the anisotropic behavior of printed parts. The proposed approach combines the learning capabilities of neural networks with the decision-making strengths of fuzzy logic, enabling the ANFIS to refine printing parameters to improve part quality. Experimental data collected from AM processes are used to train the ANFIS model, allowing it to predict outputs such as stress, strain, and Young’s modulus under various printing parameters values. The predictive performance of the model was assessed with the root mean square error (RMSE) and coefficient of determination (R²) as evaluation metrics. The study initially examined the impact of key parameters on model performance and subsequently compared two fuzzy partitioning techniques—grid partitioning and subtractive clustering—to identify the most effective configuration. The experimental results and analysis demonstrated that ANFIS could dynamically adjust key printing parameters, leading to significant improvements in the prediction accuracy of stress, strain, and Young’s modulus, showcasing its potential to address the inherent complexities of additive manufacturing processes.

Additive Manufacturing, or 3D Printing, is based on manufacturing physical objects by sequential deposition of layers of material. Although the usage of AM is growing, no straightforward methodology exists to produce parts with specific, or optimized, mechanical properties. In this work, an approach for optimizing the mechanical properties of AM specimens under bending stress is presented, using DOE. For the experimental procedure, Fused Deposition Modeling (FDM) technology was used along with Polylactic acid (PLA) as the in-process material. Nozzle temperature, printing speed, infill pattern and printing orientation were selected as manufacturing factors to be optimized to achieve so maximum load and deflection to be acquired. Both optimized sets of values were increased by 53% and 28%, respectively, and were experimentally checked to validate the accuracy of the approach.

Industrial robots offer flexibility and cost advantages in machining applications but suffer from limited structural stiffness and dynamic instability, leading to significant positional errors. This study presents a simulation-driven framework for automated toolpath compensation in robotic machining, integrating computer-aided design, manufacturing, and engineering environments. Finite Element Analysis is employed to predict stress, deformation, and reaction forces during machining. These predictions guide dynamic adjustments to key process parameters, such as feed rate and spindle speed, to optimize performance and accuracy. An automated optimization procedure streamlines this process, enhancing toolpath efficiency and safety. The framework is validated through a case study involving the machining of an aluminum support bracket using a KUKA KR3 robot. Simulation results demonstrate significant improvements in path accuracy, shorter machining time and enhanced surface quality. The enhanced toolpath achieves a 10–15% reduction in non-cutting movements, a 5–10% improvement in surface finish and a 15–25% decrease in machining time compared to the initial configuration. This approach eliminates the need for hardware modifications or real-time sensors, providing a flexible and modular solution for achieving high precision outcomes in robotic machining. The work presents an automated methodology for compensating multi-source errors, bridging the gap between virtual analysis and physical execution.

Due to the manufacturing process of Additive Manufacturing (AM) Parts the mechanical properties cannot be predicted. The scope of the present work is to propose a predictive tool for the mechanical properties of AM parts through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines neural networks with fuzzy logic to generate a mapping between the inputs and the output. Real data experiments from different AM technologies and materials compose the initial dataset so a detailed investigation is conducted to gain knowledge of how different parameters affect the UTS, the elongation and the Young modulus of each experiment. Experimental data were also use to train, check and verify the methodology. The method is tested for FFF technology, PLA material and 3 point bending stress, with interesting results.

This study presents the design and development of a wearable lower-limb exoskeleton system aimed at supporting motion assistance in monoplegia-related conditions. The proposed approach integrates a simplified sensing configuration with a data-driven neuro-fuzzy control framework based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). Motion data are acquired from the healthy limb using bend flex sensors and are used to generate control signals for the actuation of the impaired limb through an Arduino-based embedded platform. The mechanical structure is developed using a lightweight 3D-printed design combined with high-torque DC motors and gear transmission mechanisms. Experimental evaluation conducted under controlled conditions demonstrates that the system is capable of capturing and reproducing fundamental motion patterns, with the ANFIS model providing a consistent mapping between sensor inputs and actuator responses. The obtained results indicate a satisfactory level of performance for motion pattern reproduction, particularly in terms of temporal behavior and transition between movement states. The presented system emphasizes low-cost implementation, computational efficiency, and practical implementation, making it suitable as a proof-of-concept framework for wearable assistive technologies. While the results demonstrate the feasibility of the proposed approach for motion reproduction, further studies involving extended testing and user-specific adaptation are required to assess its potential applicability in real-world scenarios.