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K. Karthi,
T. Kamaleshwar,
D. Kirubakaran,
G. Swetha,
N. Kanagavalli,
M. Kavitha,
Anumula Sruthi,
- Assistant Professor, Department of Electrical and Electronics Engineering, V. S. B. Engineering College, Karur, Tamil Nadu, India
- Associate Professor, Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, Tamil Nadu, India
- Senior Assistant Professor, Department of Computer Science & Engineering, CVR College of Engineering, Hyderabad, Telangana, India
- Associate Professor, Department of Computer Science & Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
- Associate Professor, Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
- Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaih Education Foundation, Andhra Pradesh, India
Abstract
Artificial intelligence (AI) has proven an efficient method to optimize the design and manufacture of polymer nanocomposites, allowing the proper prediction of the behavior of the materials and the results of the processing. This work proposes an AI-based framework to enhance the performance characteristics, thermal stability and manufacturing performance of advanced polymer nanocomposites. The input variables of the proposed framework are the material composition, the nanoparticle concentration, the particle size, the matrix properties, the processing temperature, the curing conditions, the pressure and environment parameters. Machine learning technologies are employed to elucidate the complex interactions between processing parameters, nanocomposite structure and ultimate performance. The prediction models are assessed in terms of accuracy, mean absolute error, root mean square error and coefficient of determination to find out the most dependable optimization technique. The approach allows predicting numerous key performance characteristics, heat deterioration resistance, processing efficiency and manufacturing quality with less dependence on full experimental tests. AI based optimization also allows to find ideal material compositions and processing settings for desired higher multi-functional performance. The proposed approach provides a scalable and viable avenue to speed nanocomposite development, reduce manufacturing cost, improve process consistency, and enable data-driven material design. The results indicate that AI has the potential to improve the creation of sustainable high-performance polymer nanocomposites for advanced engineering applications.
Keywords: Thermal Stability; Performance Optimization; Manufacturing Performance; Nanoparticle Reinforcement; Predictive Modeling
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Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 18/08/2026 | |
| Accepted | 10/09/2026 | |
| Published | 18/09/2026 | |
| Publication Time | 31 Days |