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R. Anandhan,
L.C. Manikandan,
V. Sathya,
A Mohana Priya,
S.P. Vijaya Vardan Reddy,
D. Ruban Thomas,
D. Jayalakshmi,
- Assistant Professor, Department of Mechanical Engineering, Dhanalakshmi Srinivasan Engineering College (Autonomous), Perambalur, Tamil Nadu, India
- Professor, Department of Computer Science and Engineering, Universal Engineering College, Vallivattom, Thrissur, Kerala, 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
- Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology Kancheepuram, Tamil Nadu, India
- Associate Professor, Department of Electronics and Communication Engineering (ACT), R.M.K. Engineering College, Chennai, Tamil Nadu, India
- Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering, R.M.D Engineering College, Gummidipondi, Thiruvallur, Tamil Nadu, India
Abstract
The proposed architecture merges current polymer composite engineering with 5G connection and edge computing to enable sustainable and intelligent manufacturing. The idea is to manufacture lightweight, durable and high-performance polymer composites while minimizing the material waste, energy use and environmental influence throughout the production process. Smart sensors are used for continuous tracking of parameters including temperature, pressure, curing parameters, material composition and production speed inside the fabrication environment. 5G connectivity allows those data to be sent in real-time to edge computer nodes close to the production equipment. With edge analytics we can spot abnormalities, make operations more efficient, process data as it comes in, and make choices more rapidly without depending exclusively on offsite cloud infrastructure. The machine learning algorithms can also detect process irregularities and anticipate material quality variations. This enables corrective action to be taken in a timely way. The framework also allows regulatory oversight of manufacturing activities in accordance with environmental, safety and quality standards. Manufacturers can use automated compliance assessment to check rules on emissions, waste creation, energy use and material handling. The suggested system can exploit intelligent sensing, high-speed connectivity, localized computing and sustainable fabrication techniques to boost manufacturing efficiency, quality consistency, environmental responsibility and regulatory transparency. This integrated design is the basis for future smart manufacturing of green polymer composites.
Keywords: Intelligent Polymer Composites, Eco-Friendly Production, Rapid Fabrication, Sustainable Manufacturing, Regulatory Monitoring, Real-Time Process Optimization.
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Journal of Polymer & Composites
| Volume | 14 | |
| 05 | ||
| Received | 30/09/2026 | |
| Accepted | 15/10/2026 | |
| Published | 02/11/2026 | |
| Publication Time | 33 Days |