Authors :
Parthasarathi Mishra
Volume/Issue :
ICMST-2025
Google Scholar :
https://tinyurl.com/3xm2m3ej
Scribd :
https://tinyurl.com/4sbfrw7v
DOI :
https://doi.org/10.38124/ijisrt/25nov756
Abstract :
The advancement of fiber-reinforced composites has revolutionized the design and development of high-
performance materials for aerospace, automotive, and structural applications. However, the complexity of composite
systems—arising from heterogeneous microstructures, multiple reinforcement mechanisms, and nonlinear mechanical
behavior—poses challenges in predictive modeling and performance optimization. Recent developments in artificial
intelligence (AI) and computational intelligence (CI) have enabled efficient modeling, simulation, and optimization of these
materials by integrating machine learning, deep learning, and evolutionary algorithms. This study reviews and analyzes
AI-driven methods applied to fiber-reinforced metal matrix composites (MMCs) and polymer matrix composites (PMCs),
emphasizing data-driven prediction of mechanical, thermal, and tribological properties. The paper further explores
hybrid computational models combining finite element analysis (FEA) with neural networks, genetic algorithms (GA), and
fuzzy logic to achieve enhanced predictive accuracy and process parameter optimization. The proposed framework
demonstrates the potential of AI–CI methods in accelerating material design cycles and optimizing composite fabrication
processes with improved precision, reduced experimental cost, and higher reliability.
Keywords :
Artificial Intelligence, Computational Intelligence, Fiber-Reinforced Composites, Metal Matrix Composites, Polymer Matrix Composites, Machine Learning, Optimization, Neural Networks.
The advancement of fiber-reinforced composites has revolutionized the design and development of high-
performance materials for aerospace, automotive, and structural applications. However, the complexity of composite
systems—arising from heterogeneous microstructures, multiple reinforcement mechanisms, and nonlinear mechanical
behavior—poses challenges in predictive modeling and performance optimization. Recent developments in artificial
intelligence (AI) and computational intelligence (CI) have enabled efficient modeling, simulation, and optimization of these
materials by integrating machine learning, deep learning, and evolutionary algorithms. This study reviews and analyzes
AI-driven methods applied to fiber-reinforced metal matrix composites (MMCs) and polymer matrix composites (PMCs),
emphasizing data-driven prediction of mechanical, thermal, and tribological properties. The paper further explores
hybrid computational models combining finite element analysis (FEA) with neural networks, genetic algorithms (GA), and
fuzzy logic to achieve enhanced predictive accuracy and process parameter optimization. The proposed framework
demonstrates the potential of AI–CI methods in accelerating material design cycles and optimizing composite fabrication
processes with improved precision, reduced experimental cost, and higher reliability.
Keywords :
Artificial Intelligence, Computational Intelligence, Fiber-Reinforced Composites, Metal Matrix Composites, Polymer Matrix Composites, Machine Learning, Optimization, Neural Networks.
