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

Journal of Polymer & Composites Original Research

Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms

  1. Harish Reddy Gantla Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad
  2. Kasthuri Rajendra Prasad Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad
  3. Anirbit Sengupta Department of Computer Science and Engineering – AI, Brainware University
  4. Biyyapu Vishnu Priya Department of Computer Science and Engineering, QIS College of Engineering and Technology
  5. Manna Sheela Rani Chetty Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Green Fields, Vaddeswaram, Guntur
  6. Parul Goyal Department of Computer Science & Engineering, M.M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala

Abstract

The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage signatures during fatigue testing. Extracted features are processed through a hybrid learning architecture, wherein XGBoost ranks and selects key features, and Long Short-Term Memory (LSTM) networks perform temporal modeling for accurate life prediction. The model is implemented on a cloud-based IoT platform with support for real-time inference and visualization in dashboards. Experimental results show H-LiProNet to outperform the standard models like Miner's Rule, Support Vector Regression (SVR), and single LSTM by a big margin with an RMSE of 580 cycles and R² of 0.92. The model was very precise even in the presence of artificial noise (σ = 0.05), validating the model strength. Statistical tests (p < 0.05) determined significance of the performance gains. This study is among the first to combine hybrid AI modeling, real-time sensor fusion, and cloud-based deployment for predictive maintenance of FRP composites. The approach enables intelligent, adaptive lifecycle monitoring applicable to aerospace, civil, and renewable energy sectors.

Keywords

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