STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

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

  1. N Sreekanth Department of Electronics and Communication Engineering, G. Pullaiah College of Engineering and Technology, Kurnool
  2. Gom Taye Department of Computer Science & Engineering, Rajiv Gandhi University, Doimukh
  3. Harish Chandra Mohanta Department of Electronics and Communication Engineering, Centurion University of Technology and Management
  4. Megha Gupta Department of Computer Science & Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies
  5. Rashid Hashmi Sharda School of Media Film & Entertainment, Sharda University
  6. D. Naga Malleswari Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Green Fileds, Vaddeswaram

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

References (30)

  1. Ajvazi E, Schinegger V, Bauer F, Drechsler D, Rettenwander P, Kaineder D, et al. Dynamic Covalent Polymer Networks of Silicone Elastomers via Organoborane Lewis‐Pairs. Chemistry – A European Journal. 2025;31(39). doi:10.1002/chem.202501595
  2. Dai M, Han X, Zhang H, Yan J, Han R, Que L, et al. Visible light-initiated rapid self-healing of PDMS elastomers engineered through dual dynamic bonding networks for smart sensors. Materials Horizons. 2025;12(16):6143-6154. doi:10.1039/d5mh00655d
  3. Wu Z, Chu C, Jin Y, Yang L, Qian B, Wang Y, et al. Dynamic cross-linked topological network reconciles the longstanding contradictory properties of polymers. Science Advances. 2025;11(12). doi:10.1126/sciadv.adt0825
  4. Khan I, Al Rashid A, Koç M. Integration of machine learning and digital twin in additive manufacturing of polymeric-based materials and products. Progress in Additive Manufacturing. 2025;10(12):10685-10737. doi:10.1007/s40964-025-01257-4
  5. Chen L, Ning N, Zhou G, Li Y, Feng S, Guo Z, et al. Bio-Based and Solvent-Free Epoxy Vitrimers Based on Dynamic Imine Bonds with High Mechanical Performance. Polymers. 2025;17(5):571. doi:10.3390/polym17050571
  6. Chaparro-Cárdenas SL, Ramirez-Bautista JA, Terven J, Córdova-Esparza DM, Romero-Gonzalez JA, Ramírez-Pedraza A, et al. A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare. Healthcare. 2025;13(14):1763. doi:10.3390/healthcare13141763
  7. Karuppusamy M, Kalidas S, Palanisamy S, Nataraj K, Nandagopal RK, Natarajan R, et al. Real-time monitoring in polymer composites: Internet of things integration for enhanced performance and sustainability—A review. BioResources. 2025;20(3):8093–8118.
  8. Chen L, Ning N, Zhou G, Li Y, Feng S, Guo Z, et al. Bio-Based and Solvent-Free Epoxy Vitrimers Based on Dynamic Imine Bonds with High Mechanical Performance. Polymers. 2025;17(5):571. doi:10.3390/polym17050571
  9. SRM Institute of Science and Technology,Vadapalani,Chennai,India, YESHWANTH J. Cyber Physical Echoes - Harnessing Digital Twin Intelligence for Real Time System Optimization. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. 2024;08(11):1-7. doi:10.55041/ijsrem38489
  10. Alajmi A. Twinning the future: Implementing digital twin technology in the optimisation of fibre-reinforced polymers. MATEC Web of Conferences. 2024;401:11005. doi:10.1051/matecconf/202440111005
  11. Xu X, Wang G, Yan H, Zhang L, Yao X. Deep-learning-enhanced digital twinning of complex composite structures and real-time mechanical interaction. Composites Science and Technology. 2023;241:110139. doi:10.1016/j.compscitech.2023.110139
  12. Zhao B, Dang Q, Zhang Q, Du C, Li C. Intelligent Monitoring and Scheduling of Real-time Dynamic Data Based on Digital Twin. 2023 3rd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT). 2023:536-541. doi:10.1109/icfeict59519.2023.00094
  13. Chambon A, Sahli A, Rachedi A, Mebarki A. Optimizing IoT Networks Deployment Under Connectivity Constraint For Dynamic Digital Twin. 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom). 2023:474-480. doi:10.1109/metacom57706.2023.00088
  14. Lei Z, Chen H, Huang S, Wayment LJ, Xu Q, Zhang W. New Advances in Covalent Network Polymers via Dynamic Covalent Chemistry. Chemical Reviews. 2024;124(12):7829-7906. doi:10.1021/acs.chemrev.3c00926
  15. Kamarulzaman S, Salleh MR, Abdullah AH. Covalent adaptable networks from renewable resources. Mater Today Chem. 2023;30:101656. doi:10.1016/j.mtchem.2023.101656.
  16. Schenk V, Labastie K, Destarac M, Olivier P, Guerre M. Vitrimer composites: current status and future challenges. Materials Advances. 2022;3(22):8012-8029. doi:10.1039/d2ma00654e
  17. Li L, Peng X, Zhu D, Zhang J, Xiao P. Recent Progress in Polymers with Dynamic Covalent Bonds. Macromolecular Chemistry and Physics. 2023;224(20). doi:10.1002/macp.202300224
  18. Lucherelli MA, Turri C, Guadagno L. Biobased vitrimers: Towards sustainable and adaptable covalent adaptable networks. Prog Polym Sci. 2022;135:101484. doi:10.1016/j.progpolymsci.2022.101484.
  19. Alajmi A, Al-Ghamdi H, Almousa N. Implementing digital twin technology in the optimisation of fibre-reinforced polymers. In: MATEC Web Conf. 2024;379:02010. doi:10.1051/matecconf/202437902010.
  20. Ren J, Qin M, Sun Y. Industrial applications of digital twins: A systematic review. J Manuf Syst. 2025;74:405–423. doi:10.1016/j.jmsy.2025.04.005.
  21. Zhang H, Li Y, Zhang S, Yang Y. A decade of digital twins in materials science and engineering: Advances and challenges. Chin J Mech Eng. 2025;38:40. doi:10.1186/s10033-025-01210-0.
  22. Zhang Y, Liu W, Wang T. Advances in sensor technologies for composites structural health monitoring. Compos Sci Technol. 2025;260:110066. doi:10.1016/j.compscitech.2025.110066.
  23. Murugan SP, Subramanian K, Ramesh G. Strain measurements in polymer composites with embedded fiber Bragg gratings. Results Phys. 2022;34:105331. doi:10.1016/j.rinp.2022.105331.
  24. Lopes C, Pinho A, Silva F. Smart CFRP composites for damage sensing and online structural health monitoring. Polymers (Basel). 2024;16(19):2698. doi:10.3390/polym16192698.
  25. Müller M, Gärtner A, Becker T. Wireless, material-integrated sensors for strain and temperature monitoring in GFRP. Sensors (Basel). 2023;23(14):6375. doi:10.3390/s23146375.
  26. Patel R, Singh J, Kumar P. Real-time monitoring in polymer composites: IoT integration and performance evaluation. BioResources. 2025;20(1):552–570.
  27. Kim H, Choi H, Kang D, Lee WB, Na J. Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry. Chemical Science. 2024;15(21):7908-7925. doi:10.1039/d3sc05281h
  28. Karpovich C, Pan E, Olivetti EA. Deep reinforcement learning for inverse inorganic materials design. npj Computational Materials. 2024;10(1). doi:10.1038/s41524-024-01474-5
  29. Würz V, Holzer P, Böhlke T. Inverse material design using deep reinforcement learning with homogenization. Comput Methods Appl Mech Eng. 2025;424:116886. doi:10.1016/j.cma.2024.116886.
  30. Roh S, Nam Y, Nguyen MTN, Han JH, Lee JS. Dynamic Covalent Bond-Based Polymer Chains Operating Reversibly with Temperature Changes. Molecules. 2024;29(14):3261. doi:10.3390/molecules29143261
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