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K. Arun Kumar,
Anumula Sruthi,
N. Bala Sundara Ganapathy,
B. Anni Princy,
E. Sivajothi,
Duvvuri Eswara Chaitanya,
J. Jeya Caleb,
- Assistant Professor, Department of Electronics and Communication Engineering, Loyola Institute of Technology, Chennai, Tamil Nadu, India
- Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaih Education Foundation, Andhra Pradesh, India
- Professor, Department of Information Technology, Panimalar Engineering College, Chennai, Tamil Nadu, India
- Professor, Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sagunthala Engineering College, Chennai, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
- Associate Professor, Department of Electronics and Communication Engineering, R.V.R & J.C College of Engineering, Guntur, Andhra Pradesh, India
- Assistant Professor, Department of Electronics and Communication Engineering, Easwari Engineering College, Ramapuram, Chennai, Tamil Nadu, India
Abstract
The design of the material, the prediction of the properties and the fabrication techniques should be enhanced to efficiently use the high performance polymer nanocomposites. However, the traditional computational methods are often restricted by issues of scaling, computing cost, data integration, and processing time in assessing the complex combinations of polymers, nanofillers, processing factors and material characteristics. This work presents a scalable cloud-computing framework for the efficient design and fabrication of advanced materials polymer nanocomposites. We blend cloud based data management, machine learning, computational modeling and process optimization to speed up the prediction and optimization of nanocomposite properties. Distributed cloud resources are used to process experimental and simulation based datasets comprising polymer composition, nanofiller properties, processing parameters and performance target metrics. Machine learning algorithms are trained to correlate processing and composition parameters to mechanical, thermal, electrical and barrier properties. The framework also provides optimization algorithms to determine the optimal material formulations and manufacturing settings, minimizing the computational and experimental effort. Cloud parallel processing enables scalable analysis of large datasets and collaborative creation of items. The suggested method provides a broad computational platform for the rapid material screening, performance prediction and production parameter optimization. Hence, the framework offers data-driven design and manufacturing efficiency for the creation of high performance, light-weight, sustainable and application specific polymer nanocomposites with enhanced precision.
Keywords: Material Design, Fabrication Optimization, Predictive Modeling, Computational Framework, Sustainable Materials
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Journal of Polymer & Composites
| Volume | 14 | |
| 05 | ||
| Received | 07/09/2026 | |
| Accepted | 08/10/2026 | |
| Published | 13/10/2026 | |
| Publication Time | 36 Days |