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1. Dehydrofluorinated PVDF for structural health monitoring in fiber reinforced composites

   https://doi.org/https://doi.org/10.1016/j.compscitech.2021.108982  

   Groo, Lori Anne ; Inman, Daniel ; Sodano, Henry ( September 2021 , Composites science and technology)

   null (Ed.)

   Structural health monitoring of fiber reinforced composites is an extensive field of research that aims to reduce maintenance costs through in-situ damage detection. However, the need for externally bonded sensor systems and complicated fabrication processes limit the widespread application of most current structural health monitoring techniques. This work introduces a novel multifunctional fiber reinforced composite that relies on a ferroelectric prepreg fabricated using dehydrofluorinated (DHF) polyvinylidene fluoride (PVDF), which exhibits a thermally stable piezoelectric response. The self-sensing material presented in this work requires minimal external components, as the piezoelectric sensing mechanism is fully contained within the composite. This is accomplished by fabricating a ferroelectric prepreg consisting of DHF PVDF infused woven fiberglass, which is sandwiched between woven carbon fabric layers that act as electrodes, thus forming a piezoelectric sensor fabricated with entirely structural composite materials. Notably, the sensing material is a fully distributed prepreg rather than discretely embedded sensors which enables simplified monitoring of complex structures. As the composite experiences damage under flexural and tensile loading, the internal change in strain results in a charge separation that is detectable as a voltage emission across the sample electrodes. The self-sensing capabilities of this material are explored using traditional mechanical testing techniques, showing comparable performance to common damage detection methods, all while eliminating the need for external bonding of sensors to the structure. 

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2. Dehydrofluorinated PVDF for Structural Health Monitoring in Fiber Reinforced Composites

   Groo, L. ( September 2021 , Composites science and technology)

   null (Ed.)

   Structural health monitoring of fiber reinforced composites is an extensive field of research that aims to reduce maintenance costs through in-situ damage detection. However, the need for externally bonded sensor systems and complicated fabrication processes limit the widespread application of most current structural health monitoring techniques. This work introduces a novel multifunctional fiber reinforced composite that relies on a ferroelectric prepreg fabricated using dehydrofluorinated (DHF) polyvinylidene fluoride (PVDF), which exhibits a thermally stable piezoelectric response. The self-sensing material presented in this work requires minimal external components, as the piezoelectric sensing mechanism is fully contained within the composite. This is accomplished by fabricating a ferroelectric prepreg consisting of DHF PVDF infused woven fiberglass, which is sandwiched between woven carbon fabric layers that act as electrodes, thus forming a piezoelectric sensor fabricated with entirely structural composite materials. Notably, the sensing material is a fully distributed prepreg rather than discretely embedded sensors which enables simplified monitoring of complex structures. As the composite experiences damage under flexural and tensile loading, the internal change in strain results in a charge separation that is detectable as a voltage emission across the sample electrodes. The self-sensing capabilities of this material are explored using traditional mechanical testing techniques, showing comparable performance to common damage detection methods, all while eliminating the need for external bonding of sensors to the structure. 

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3. Sub-microscale speckle pattern creation on single carbon fibers for scanning electron microscope-digital image correlation (SEM-DIC) experiments

   Shah, Karan ; Sockalingam, Subramani ; O'Brien, Hannah ; El Loubani, Mohammad ; Lee, Dongkyu ; Sutton, Michael ( November 2022 , Composites Part A Applied science and manufacturing)

   High performance carbon fibers are widely used as fiber reinforcements in composite material systems for aerospace, automotive, and defense applications. Longitudinal tensile failure of such composite systems is a result of clustering of single fiber tensile failures occurring at the microscale, on the order of a few microns to a few hundred microns. Since fiber tensile strength at the microscale has a first order effect on composite strength, it is important to characterize the strength of single fibers at microscale gage lengths which is extremely challenging. An experimental technique based on a combination of transverse loading of single fibers under SEM with DIC is a potential approach to access microscale gage lengths. The SEM-DIC technique requires creation of uniform, random, and contrastive sub-microscale speckle pattern on the curved fiber surface for accurate strain measurements. In this paper, we investigate the formation of such sub-microscale speckle patterns on individual sized IM7 carbon fibers of nominal diameter 5.2 µm via sputter coating. Various process conditions such as working pressure, sputtering current, and coating duration are investigated for pattern creation on fiber surface using a gold-palladium (Au-Pd) target. A nanocluster type sub-microscale pattern is obtained on the fiber surface for different coating conditions. Numerical translation experiments are performed using the obtained patterns to study image correlation and identify a suitable pattern for SEM-DIC experiments. The pattern obtained at a working pressure of 120–140 mTorr with 50 mA current for a duration of 10 min is found to have an average speckle size of 53 nm and good contrast for image correlation. Rigid body translation SEM experiments for drift/distortion correction using a sized IM7 carbon fiber coated with the best patterning conditions showed that Stereo-SEM-DIC is needed for accurately characterizing fiber strain fields due to its curved surface. The effect of sputter coating on fiber tensile strength and strain is investigated via single fiber tensile tests. Results showed that there is no significant difference in the mean tensile strength and failure strain between uncoated and coated fibers (average increment in fiber diameter of ∼221 nm due to coating) at 5% significance level. SEM images of failure surfaces for uncoated and coated fibers also confirmed a tensile failure of fibers as observed for polyacrylonitrile PAN-based fibers in literature. 

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4. Artificial Neural Networks and Phenomenological Degradation Models for Fatigue Damage Tracking and Life Prediction in Laser Induced Graphene Interlayered Fiberglass Composites

   Nasser, J. ( June 2021 , Smart materials and structures)

   null (Ed.)

   The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates the potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs. 

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5. Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites

   https://doi.org/https://doi.org/10.1088/1361-665X/ac093d  

   Nasser, Jalal ; Groo, Lori Anne ; Sodano, Henry ( June 2021 , Smart materials and structures)

   null (Ed.)

   The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates the potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs. 

   more » « less