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Journal of Polymer & Composites Original Research

Explainable Digital-Twin Guided Self-Healing Biohybrid Polymer Nanocomposites for Autonomous Structural Health Monitoring in Aerospace Applications

  1. V. L. Manasa Mandalapu Department of Electronics and Communication Engineering., School of Engineering and Technology, Mohan Babu University
  2. P. Venkatramana Department of Electronics and Communication Engineering., School of Engineering and Technology, Mohan Babu University
  3. E. Baby Anitha Department of Computer Science and Engineering, K.S.R College of Engineering, Tiruchengode, Namakkal Dt.
  4. S Vinayagapriya Department of Electronics and Communication Engineering., St. Joseph's College of Engineering, Chennai
  5. D. Suresh Department of Mechanical Engineering, Indra Ganesan college of engineering, Trichy- 12
  6. Bibhu Prasad Ganthia Department of Electrical Engineering, Indira Gandhi Institute of Technology, Dhenkanal

Abstract

Concurrently, lightweight, multifunctional, damage-tolerant materials which can be used for continuous monitoring of the structural integrity under complex mechanical and environmental conditions are required for aerospace structures. In this paper, a hybrid bio-polymer nanocomposite based Explainable Digital-Twin Guided Self-Healing (EDT-SH-BPN) approach for autonomous structural health monitoring (SHM) and recovery in aerospace applications is proposed. The proposed system comprises a bio-derived epoxy/epoxidized natural rubber (ENR) matrix, embedded electrical sensing and self-healing capabilities, a physics-based digital twin model and explainable artificial intelligence (XAI), within a closed-loop sense–diagnose–predict–heal–verify framework. The developed nanocomposite exhibits significant enhancement in comparison to neat bio-epoxy/ENR matrix with the following mechanical properties: tensile strength (91.4 MPa), Young's modulus (3.26 GPa), flexural strength (126.8 MPa), interlaminar shear strength (51.7 MPa), and impact strength (34.5 kJ/m2). The composite has a glass-transition temperature of 118.7 °C, a storage modulus of 3.48 GPa, and a thermal conductivity of 1.84 W/m·K. It is found that the MXene–GNP conductive network has 42.6 S/m conductivity, a gauge factor of 6.84, crack-detection sensitivity of 85 μm and response time of 18.4 ms, with a high sensing repeatability of 96.1% for 1000 cycles. Self-healing can decrease the crack width from 420 to 58 μm after 6 h, which is 86.2% crack closure and 87.6% healing efficiency. The digital-twin/XAI framework can be accurate at 97.6% in damage classification, 97.0% in F1 score, 3.1% in damage severity MAE, 1.7 mm in localization error, 96.8% in healing decision accuracy and 93.6% in explanation consistency. The results clearly prove the capability of EDT-SH-BPN in lightweight, intelligent, predictive and autonomous aerospace structural systems.

Keywords

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