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.
Fiber-reinforced polymer matrix composites deteriorate mechanically due to fatigue degradation during cyclic stress. The progressive decrease in elastic stiffness over fatigue life is well-established and investigated, yet many dynamic engineering systems that use composite materials are subjected to random and unexpected loading circumstances, making it impossible to continually monitor such structural changes. LIG can detect strain and damage in fiberglass composites under quasi-static and dynamic loads. ANNs and traditional phenomenological models may assess damage development and fatigue life utilizing LIG interlayered fiberglass composites’ piezoresistivity. Passive experiments monitor LIG interlayered fiberglass composite elastic stiffness and electrical resistance during tension–tension fatigue stress. Electrical resistance-based damage metrics follow similar trends to elastic stiffness-based parameters and may accurately depict damage development in LIG interlayered fiberglass composites over fatigue life. In specimen-to-specimen and cycle-to-cycle schemes, trained ANNs and phenomenological degradation and accumulation models predict fiberglass composite fatigue life and damage state. In a specimen-to-specimen scheme, a two-layer Bayesian regularized ANN with 40 neurons per layer beats phenomenological degradation models by at least 60%, with R2 values more than 0.98 and RMSE values less than 10−3 . A two-layer Bayesian regularized ANN with 25 neurons per layer exhibits R2 values more than 0.99 and RMSE values less than 2×10−4 when more than 30% of the original data is used in a cycle-to-cycle method. Piezoresistive LIG interlayers and ANNs can correctly and constantly predict fatigue life in multifunctional composite structures.
Piezoresistive laser induced graphene are embedded within the interlaminar regions of fiberglass composite materials using a treatment-free and scalable transfer-printing process, all while maintaining mechanical properties. Through passive resistance measurements, the multifunctional material is demonstrated to be capable of in-situ tracking of both monotonic and cyclic strain, in addition to detecting distinct damage events under tensile and flexural loading conditions. Furthermore, The LIG interlayers are also shown to enable three-dimensional damage localization in fiberglass composites, along with the monitoring of structural damage progression and accumulation throughout fatigue life. The information can be then combined with smart prognostic algorithms, such as neural networks, in order to predict the onset of catastrophic structural failure.
There is a need for buoyancy engines to modulate sensor depth for optimal positioning and station-keeping. Compared to current technologies, Ionic Buoyancy Engines does not have any moving parts. They are energy efficient and miniaturization ready. Ionic Buoyancy Engines change their density by locally varying ionic concentrations in a closed chamber with a wall made of a semi-permeable membrane. The local change in concentration pumps water in and out of the chamber leading to buoyancy change. This study presents a model that is used to simulate the steady state controlled depth of the buoyancy engine, along with experimental results.
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