Real-Time Crack Detection in Polymer Composites Using Embedded IoT Sensor Networks

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Year : 2026 | Volume : 14 | 04 | Page :
By

Sanjeev Kumar,

P. Girish,

Pranashi Chakraborty,

Narne Sravanthi,

V. Akilandeswari,

Mohit Ghai,

  1. Professor, Department of Electronics & Communication Engineering, Aditya University, Surampalem, Andhra Pradesh, India
  2. Assistant Professor, Department of Civil Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India
  3. Assistant Professor, Department of Computer Science and Engineering -AI, Brainware University, Barasat, West Bengal, India
  4. Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India
  5. Associate Professor, Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, India
  6. Assistant Professor, Department of Computer Science, Guru Gobind Singh Indraprastha University, Delhi, India

Abstract

This study developed an embedded Internet of Things sensor network for real-time crack detection, localization, and severity assessment in glass-fiber-reinforced polymer composites. Four polyvinylidene fluoride sensors were embedded near the laminate mid-plane and connected to synchronized wireless acquisition nodes. Controlled low-velocity impacts generated graded damage states. Ultrasonic C-scan and optical microscopy provided independent labels. A compact multi-channel convolutional model processed normalized waveforms, spectral entropy, signal energy, and arrival-time differences at an edge gateway. The framework achieved a macro-F1 score of 0.938, a mean localization error of 8.9 mm, and an average end-to-end warning latency of 71 ms. Sensor-embedded laminates retained 99.1% of the flexural strength of uninstrumented controls. Multi-sensor fusion and baseline normalization produced the largest reductions in false alarms. The approach also preserved records during temporary wireless interruption through local buffering. The proposed material-aware IoT architecture allows for fast crack monitoring. It offers spatially-distributed detection of structural damage. This system can be used for continuous monitoring of polymer composites. It identifies cracks at an early stage. There are no major mechanical penalties to the architecture. It maintains the structural performance of the composite. This allows for condition-based maintenance to be performed. Overall it enhances the reliability and service life of polymer-composite structures.

Keywords: Polymer composites, crack detection, embedded sensors, Internet of Things, structural health monitoring.

How to cite this article: Sanjeev Kumar, P. Girish, Pranashi Chakraborty, Narne Sravanthi, V. Akilandeswari, Mohit Ghai. Real-Time Crack Detection in Polymer Composites Using Embedded IoT Sensor Networks. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: Sanjeev Kumar, P. Girish, Pranashi Chakraborty, Narne Sravanthi, V. Akilandeswari, Mohit Ghai. Real-Time Crack Detection in Polymer Composites Using Embedded IoT Sensor Networks. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=252982

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Ahead of Print Subscription Original Research
Volume 14
04
Received 24/07/2026
Accepted 13/08/2026
Published 20/08/2026
Publication Time 27 Days

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