Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    C. Selvarathi,

  • M. Parthiban,

  • D. Pavunraj,

  • D. Shobana,

  • V. Parimala,

  • R. Dhivya,

  • S. Palpandi,

  1. Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India
  2. Professor, Department of Computer Science and Engineering, SASI Institute of Technology and Engineering, Tadepalligudem, West Godavari District, Andhra Pradesh, India
  3. Assistant Professor, Department of Artificial Intelligence and Data Science, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
  4. Assistant Professor (S.G.), Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Electronics and Communication Engineering, Chennai Institute of Technology, Tamil Nadu, India
  6. Associate Professor, Department of Electronics and Communication Engineering, Adhiparasakthi College of Engineering, Kalavai, Tamil Nadu, India
  7. 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

Sustainable polymer materials development is a must due to resource constraints, environmental concerns, and the demand for designed materials with high performance. When it comes to material optimization, energy utilization, process unpredictability, and lifecycle sustainability, traditional polymer production methods have their challenges. Reinforcement Learning (RL), Internet of Things (IoT) monitoring, and data-driven production are utilized in the design and manufacturing of sustainable polymer materials. It is recommended to use Internet of Things (IoT) sensors to track the temperature, pressure, humidity, curing conditions, and material behavior during the various stages of polymer synthesis. On a shared analytics platform, data-driven algorithms and machine learning assess sustainability, material quality, and process efficiency. Reinforcement learning methods optimize production parameters in real-time. Mechanical efficiency, material efficiency, energy efficiency, and production consistency are all optimized by the RL agent. Algorithms applied to predictive analytics can detect defects, foretell future events, and evaluate the service life of polymers. By utilizing smart decisions and real-time monitoring, adaptive manufacturing modifies activities. Reduced carbon footprint, efficient use of resources, recyclability, and long lifespan durability are some of the benefits we offer. Experimental results show that data-driven manufacturing, monitoring facilitated by the internet of things (IoT), and RL all work together to improve the reliability, quality, and environmental performance of polymer production processes. The research claims that IoMT can hasten the creation of sustainable polymer materials, increase industrial output, and help achieve circular economy targets. Businesses and academics in the fields of smart manufacturing and advanced sustainable materials engineering may use the results.

Keywords: Data-Driven Manufacturing, Smart Manufacturing, Predictive Analytics, Lifecycle Assessment Process Optimization.

How to cite this article:
C. Selvarathi, M. Parthiban, D. Pavunraj, D. Shobana, V. Parimala, R. Dhivya, S. Palpandi. Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
C. Selvarathi, M. Parthiban, D. Pavunraj, D. Shobana, V. Parimala, R. Dhivya, S. Palpandi. Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=249145


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Ahead of Print Subscription Original Research
Volume 14
03
Received 15/06/2026
Accepted 30/06/2026
Published 06/07/2026
Publication Time 21 Days
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