Digital Twin-Driven Optimization of Dynamic Covalent Polymer Networks under Real-Time IoT Monitoring

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Year : 2026 | Volume : 14 | 01 | Page :
    By

    N Sreekanth,

  • Gom Taye,

  • Harish Chandra Mohanta,

  • Megha Gupta,

  • Rashid Hashmi,

  • D. Naga Malleswari,

  1. Professor, Department of Electronics and Communication Engineering, G. Pullaiah College of Engineering and Technology, Kurnool, Tamil Nadu, India
  2. PhD Research Scholar, Department of Computer Science & Engineering, Rajiv Gandhi University, Doimukh, Arunachal Pradesh, India
  3. Professor, Department of Electronics and Communication Engineering, Centurion University of Technology and Management, Odisha, India
  4. Associate Professor, Department of Computer Science & Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, New Delhi, India
  5. Professor, Sharda School of Media Film & Entertainment, Sharda University, Uttar Pradesh, India
  6. Associate Professor, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Green Fileds, Vaddeswaram, Andhra Pradesh, India

Abstract

The dynamic covalent polymer networks (DCPNs) has the highest allurement because of the reversibility of all of the chemicals and repetitive. The process of convalescence is delayed, the sense of source betrayal and acting relations is too strong. This transport to our material situation is an ever-refrigerated digital twin in this painting which was developed through repetition produced by constant synchronism sensors of the IoT that is constantly refined by reinforcement learning. The technological process of adaptive optimization allowed raising the effectiveness of the healing process to the level of almost ninety percent, decreasing the number of residues stress factors by almost a half and energy requirements on the outside by a third. The digital twin predicted these states with minimal error and a fine-tuning of the stimulus application was provided in real-time by the reinforcement learning controller. Ablation study made the insistence that all the layers were value adding but this is only when added together they gave the same value. It is not simply an incremental product. Once sensing and learning is implanted, the DCPNs will feel more like the less rigid objects because of their responsive and expected behavior. This change is significant to the existing uses where consistency in harsh environments is vital, including aerospace laminates with the Internet of Things-based structures. Though the observations were done only under controlled circumstances, this method is a connotation of a larger trend: polymers, which on their own, but also learn how to stay tough.

Keywords: Reinforcement learning, Digital twin, IoT-enabled sensing, Dynamic covalent polymer networks, Adaptive polymer composites.

How to cite this article:
N Sreekanth, Gom Taye, Harish Chandra Mohanta, Megha Gupta, Rashid Hashmi, D. Naga Malleswari. Digital Twin-Driven Optimization of Dynamic Covalent Polymer Networks under Real-Time IoT Monitoring. Journal of Polymer & Composites. 2026; 14(01):-.
How to cite this URL:
N Sreekanth, Gom Taye, Harish Chandra Mohanta, Megha Gupta, Rashid Hashmi, D. Naga Malleswari. Digital Twin-Driven Optimization of Dynamic Covalent Polymer Networks under Real-Time IoT Monitoring. Journal of Polymer & Composites. 2026; 14(01):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=236206


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Ahead of Print Subscription Original Research
Volume 14
01
Received 16/09/2025
Accepted 22/10/2025
Published 17/01/2026
Publication Time 123 Days
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