LSTM-based fatigue monitoring of fiberglass composites using laser-induced graphene

Boyang Chen; Adam Childress; Jalal Nasser; Michael Fisher; Henry A. Sodano

doi:10.1117/12.3010899

9 May 2024 LSTM-based fatigue monitoring of fiberglass composites using laser-induced graphene

Boyang Chen, Adam Childress, Jalal Nasser, Michael Fisher, Henry A. Sodano

Proceedings Volume 12950, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII; 1295008 (2024) https://doi.org/10.1117/12.3010899
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

This study aims to advance the field of composite material fatigue prognosis by employing Long Short-Term Memory (LSTM) neural networks for in-situ damage progression monitoring under random dynamic loading conditions. A unique approach is adopted, wherein Laser-Induced Graphene (LIG) interlayers are embedded into fiberglass composites. These LIG interlayers are innovative sensors owing to their piezoresistive properties, enabling real-time measurement of fatigue damage monitoring. The crux of this research lies in applying LSTM neural networks, specifically designed to handle time-series data, making them ideal for modeling the stochastic and unpredictable nature of fatigue loading in composite materials. Contrasting the performance of LSTM with traditional Multilayer Perceptrons (MLP), it is observed that LSTM yields superior prediction accuracy in estimating the remaining useful life (RUL) of LIG interlayered fiberglass composites. By utilizing predefined electrical resistance damage parameters, the LSTM algorithm correlates the rate of fatigue damage buildup to the impending decline in mechanical performance. This research establishes that integrating piezoresistive LIG interlayers with LSTM neural networks culminates in a robust, reliable, and closed-loop system for structural fatigue monitoring and lifecycle prediction in composite materials subjected to random dynamic loading.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Boyang Chen, Adam Childress, Jalal Nasser, Michael Fisher, and Henry A. Sodano "LSTM-based fatigue monitoring of fiberglass composites using laser-induced graphene", Proc. SPIE 12950, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII, 1295008 (9 May 2024); https://doi.org/10.1117/12.3010899

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