ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction

Year : 2026 | Volume : 14 | Special Issue 01 | Page : 188 208
    By

    Nagaraj G,

  • Vandana Ahuja,

  • P. Satish,

  • Gayathri Devi. S,

  • Puvvada Nagesh,

  • Ayesha Siddiqa,

  1. Associate Professor, Department of Mechanical Engineering, Sethu Institute of Technology, Tamil Nadu, India
  2. Associate Professor, Department of Computer Science & Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Punjab, India
  3. Associate Professor, Department of Mathematics, Aditya University, Surampalem, Tamil Nadu, India
  4. Assistant Professor, Department of Computer Science & Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
  6. Lecturer, Department of Mathematics, Southampton Middle School, Virginia, United States of America

Abstract

Fiber-reinforced polymer (FRP) composites are widely used in aerospace and structural systems; nevertheless, the potential for microcracking and fatigue-induced performance degradation remains an obstacle with respect to improved service life. Traditional self-healing methods, while performing well on a chemical level, often lack real-time diagnostic awareness and adaptive control. To circumvent this, we developed a machine-learning augmented self-healing FRP composite, in which a DCPD–Grubbs catalytic matrix was combined with IoT sensor network capability and a hybrid CNN–LSTM predictive model. This framework identifies, interprets, and performs in-situ actions, enabling the material to become an intelligent closed-loop system capable of self-healing and managing damage automatically instead of passively. Our work differs from previous studies, which focus on the simulated static analysis of FRP composites. The synergy between IoT and CNN–LSTM learns continually from the multi-sensor data and predicts failure mechanisms and severity of internal damage based on load cycles prior to mechanical failure. The reported experiments demonstrate an average healing capacity of 90.6%; an R² of 0.989 prediction accuracy; and 11 ms as the latency to decision outcomes, exceeding state-of-the-art indications for action in thermally and magnetically initiated self-healing composites. The model’s adaptive retraining preserved accuracy across multiple healing cycles without increasing energy consumption; thus, it is appropriate for use over the long term. These results constitute a paradigm shift in polymer composites design – from passive structural materials to active, data-based agents capable of self-diagnosing and repairing. The attribute proposed overcomes barriers to sustainable, low-power, and intelligent composite systems and is aligned with the Journal of Polymer Composites’ current vision for follow-on research on multifunctional and self-adaptive polymer composite architectures.

Keywords: Damage prediction, fiber-reinforced polymers, IoT sensor integration, machine learning, self-healing polymer composites.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

How to cite this article:
Nagaraj G, Vandana Ahuja, P. Satish, Gayathri Devi. S, Puvvada Nagesh, Ayesha Siddiqa. ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction. Journal of Polymer & Composites. 2026; 14(01):188-208.
How to cite this URL:
Nagaraj G, Vandana Ahuja, P. Satish, Gayathri Devi. S, Puvvada Nagesh, Ayesha Siddiqa. ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction. Journal of Polymer & Composites. 2026; 14(01):188-208. Available from: https://journals.stmjournals.com/jopc/article=2026/view=236201


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Special Issue Subscription Original Research
Volume 14
Special Issue 01
Received 30/10/2025
Accepted 06/11/2025
Published 17/01/2026
Publication Time 79 Days
144

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