V. Parimala,
K. Sudhakar,
S. Bhuvana,
Praveen Talari,
D. Ruban Thomas,
S. Sivasankaran,
S. Palpandi,
- Assistant Professor, Department of Electronics and Communication Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India
- Associate Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
- Assistant Professor (Senior Grade), Department of Cyber Security, Rajalakshmi Engineering College, Thandalam, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India
- Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
- Professor, Department of Information Technology, Raak College of Engineering and Technology, G.N Palayam, Puducherry, India
- Assistant Professor (Senior Grade), Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
Abstract
The exceptional mechanical properties, design flexibility, and lightweight nature of polymer composite and nanocomposite materials make them indispensable in a wide range of applications, including aerospace, automotive, construction, biomedical, and energy sectors. The optimization of the strength, durability, and manufacturing efficiency of polymer composite and nanocomposite materials is highly challenging because their performance depends on matrix composition, reinforcement type, fiber or nanoparticle distribution, interfacial interactions, processing conditions, and environmental factors. Conventional optimization approaches require extensive experimentation, computational resources, and development time, leading to increased cost and reduced efficiency. Recent advances in artificial intelligence (AI) have enabled innovative data-driven approaches for improving the performance, design, and manufacturing of polymer composite and nanocomposite materials. Machine learning, deep learning, and predictive analytics are employed to model complex nonlinear relationships among material composition, processing parameters, and performance characteristics using large-scale material datasets. The proposed AI-driven framework accurately predicts key material properties, including mechanical strength, fatigue resistance, thermal stability, and long-term durability, while simultaneously optimizing curing conditions, manufacturing processes, material utilization, and defect detection. Furthermore, AI-assisted optimization shortens material design cycles, enhances production monitoring, minimizes manufacturing costs, and improves product reliability. The integration of artificial intelligence with polymer composite and nanocomposite engineering accelerates the discovery of advanced materials and supports the development of sustainable, lightweight, and high-performance composite systems. Overall, AI-driven methodologies significantly enhance the structural performance, durability, and manufacturing efficiency of next-generation polymer composite and nanocomposite materials, making them promising candidates for future engineering applications.
Keywords: Polymer nanocomposites, performance optimization, mechanical strength, durability, manufacturing efficiency, predictive analytics, material engineering.
[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]
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Journal of Polymer & Composites
| Volume | 14 | |
| Special Issue | 04 | |
| Received | 03/08/2026 | |
| Accepted | 20/08/2026 | |
| Published | 26/08/2026 | |
| Publication Time | 23 Days |