Development of Neuromorphic Polymer Composites Using IoT Sensing and Brain-Inspired Learning Algorithms

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

    Praveena Murala,

  • Pranashi Chakraborty,

  • P. Devendran,

  • T. Venkat Narayan Rao,

  • G. Anil Kumar,

  • G. Nagaraj,

  1. Assistant Professor, Department of Computer Science and Engineering, QIS College of Engineering and Technology, Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science Engineering-AI, Brainware University, Barasat, West Bengal, India
  3. Assistant Professor, Department of Robotics and Automation, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
  4. Professor, Department of Artificial Intelligence & Machine Learning, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, Telangana, India
  5. Assistant Professor, Department of Physics, Sreenidhi Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India
  6. Associate Professor, Department of Mechanical Engineering, Sethu Institute of Technology, Tamil Nadu, India

Abstract

This research aims to develop neuromorphic polymer composites by combining conductive sensing materials, IoT-based sensing data collection and brain-inspired learning models for adaptive response. Hybrid conductive polymer composites were developed by adding carbon nanofibers and graphene Nano platelets to a thermoplastic polymer. IoT sensors (strain, temperature) were employed to collect real-time sensing data that was combined with environmental data. A material-aware neuromorphic learning algorithm was created with event-driven spike coding and reward-based learning. Dynamic mechanical and environmental loads were applied. The composite exhibited consistent piezoresistive properties with a sensitivity of 0.034 %⁻¹ and repeatability of more than 97%. The proposed method achieved an accuracy of 95.8% ± 1.2%, which is much better than the conventional machine learning (89.3%) and deep learning (92.6%) methods. The response time was also reduced to 120 ms, which is a 40-50% improvement over other methods. The power consumption was also reduced by almost 35-40% in event-driven mode. Multimodal fusion also resulted in a more consistent signal with the variance reduced by ~20%. This research introduces a novel approach to the integration of polymer composites, IoT sensor technology and neuromorphic learning in an experiment. The approach provides a real-time adaptive intelligence, beyond the existing approach which decouples these technologies. The paper presents a scalable solution for smart materials with sensing and learning.

Keywords: Neuromorphic polymer composites, IoT-enabled sensing, Spiking neural networks, Smart materials, Adaptive learning.

How to cite this article:
Praveena Murala, Pranashi Chakraborty, P. Devendran, T. Venkat Narayan Rao, G. Anil Kumar, G. Nagaraj. Development of Neuromorphic Polymer Composites Using IoT Sensing and Brain-Inspired Learning Algorithms. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Praveena Murala, Pranashi Chakraborty, P. Devendran, T. Venkat Narayan Rao, G. Anil Kumar, G. Nagaraj. Development of Neuromorphic Polymer Composites Using IoT Sensing and Brain-Inspired Learning Algorithms. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=244800


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Ahead of Print Subscription Original Research
Volume 14
03
Received 08/05/2026
Accepted 20/05/2026
Published 22/05/2026
Publication Time 14 Days
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