Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens

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 re...

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Bibliographic Details
Published in:Applied sciences 2025-01, Vol.15 (1), p.7
Main Authors: Mantalas, Emmanouil-Marinos, Sagias, Vasileios D., Zacharia, Paraskevi, Stergiou, Constantinos I.
Format: Article
Language:English
Subjects:
3-D printers
3D printing
Accuracy
adaptive neuro-fuzzy inference system (ANFIS)
Additive manufacturing
Artificial intelligence
Carbon fibers
Design
Efficiency
Fuzzy logic
Fuzzy sets
intelligent manufacturing systems
Machine learning
Mechanical properties
mechanical properties prediction
Neural networks
Tensile strength
Variables
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app15010007