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1. Laser induced graphene for in situ damage sensing in aramid fiber reinforced composites

   https://doi.org/https://doi.org/10.1016/j.compscitech.2020.108541  

   Groo, Lori Anne ; Nasser, Jalal ; Inman, Daniel ; Sodano, Sodano ( January 2021 , Composites science and technology)

   null (Ed.)

   In situ monitoring of strain and damage in fiber-reinforced composites provides critical information regarding the state of the material without requiring the structure to be removed from operation. In order to avoid the use of complex, heavy, and bulky sensor networks to track the state of the structure, recent focus has turned to multifunctional materials with inherent characteristics which enable in situ monitoring. This work investigates laser induced graphene (LIG) integrated within aramid fiber reinforced composites for damage and strain sensing during mechanical loading. The LIG used here fully integrates the sensing material within the composite as the piezoresistive graphene layer is coated directly onto the reinforcing aramid fabric prior to infusing the fibers with the supporting matrix. The sensing element is thus not susceptible to environmental effects and adds no extra weight while also maintaining the specific strength of the material. As strain and damage occur within the composite, the LIG proves capable of tracking strain and detecting plastic deformation in situ. Thus, the result of this work is the integration of a multifunctional component into aramid composites which possesses in situ sensing capabilities. Furthermore, the processes and materials are easily scalable for the large-scale production of multifunctional aramid fiber reinforced composites. 

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2. 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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3. 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. 

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4. Fatigue damage tracking and life prediction of fiberglass composites using a laser induced graphene interlayer

   https://doi.org/https://doi.org/10.1016/j.compositesb.2021.108935  

   Groo, Lori Anne ; Nasser, Jalal ; Inman, Daniel ; Sodano, Henry ( August 2021 , Composites)

   null (Ed.)

   Fiberglass-reinforced composite materials are commonly used in engineering structures subjected to dynamic loading, such as wind turbine blades, automobiles, and aircraft, where they experience a wide range of unpredictable operating conditions. The ability to monitor these structures while in operation and predict their remaining structural life without requiring their removal from service has the potential to drastically reduce maintenance costs and improve reliability. This work exploits piezoresistive laser induced graphene (LIG) integrated into fiberglass-reinforced composites for in-situ fatigue damage monitoring and lifespan prediction. The LIG is integrated within fiberglass composites using a transfer-printing process that is scalable with the potential for automation, thus reducing barriers for widespread application. The addition of the conductive LIG within the traditionally insulating fiberglass composites enables direct in-situ damage monitoring through simple passive resistance measurements during tension-tension fatigue loading. The accumulation and propagation of structural damage are detected throughout the fatigue life of the composite through changes to the electrical resistance measurements, and the measurement trends are further used to predict the onset of catastrophic composite failure. Thus, this work results in a scalable and multifunctional composite material with self-sensing capabilities for potential use in high-performing, dynamic, and flexible composite structures. 

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5. A multifunctional hybrid extrinsic–intrinsic self-healing laminated composites

   https://doi.org/10.1088/1361-665X/acd82c  

   Konlan, John ; Feng, Xiaming ; Li, Guoqiang ( June 2023 , Smart Materials and Structures)

   Damage healing in fiber reinforced thermoset polymer composites has been generally divided into intrinsic healing by the polymer itself and extrinsic healing by incorporation of external healing agent. In this study, we propose to use a hybrid extrinsic-intrinsic self-healing strategy to heal delamination in laminated composite induced by low velocity impact. Especially, we propose to use an intrinsic self-healing thermoset vitrimer as an external healing agent, to heal delamination in laminated thermoset polymer composites. To this purpose, we designed and synthesized a new vitrimer, machined it into powders, and strategically sprayed a layer of vitrimer powders at the interface between the laminas during manufacturing. Also, a thermoset shape memory polymer with fire-proof property was used as the matrix. As a result, incorporation of about 3% by volume of vitrimer powders made the laminate exhibit multifunctionalities such as repeated delamination healing, excellent shape memory effect, improved toughness and impact tolerance, and decent fire-proof properties. In particular, the novel vitrimer powder imparted the laminate with first cycle and second cycle delamination healing efficiencies of 98.06% and 85.93%, respectively. The laminate also exhibited high recovery stress of 65.6 MPa. This multifunctional composite laminate has a great potential in various engineering applications, for example, actuators, robotics, deployable structures, and smart fire-proof structures.

    

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