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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. Damage monitoring in hybrid composites under projectile impact loading

   https://doi.org/10.1177/00219983251329106  

   Lincon, Mazharul Islam; Eichenlaub, Jeannine; Encarnacion, Anthony; Shonar, Mohammed; Chalivendra, Vijaya (August 2025, Journal of Composite Materials)

   This study investigates the high-velocity impact mechanics in correlation with piezo-resistance damage sensing characteristics of glass/carbon hybrid composites under projectile impact loading. Inter-ply and Intra-ply hybrid composites consisting of different ply orientations, stacking sequences, and liquid metal (LM) compositions (1 and 2 wt%) are considered for this study. An in-house one-stage gas gun setup is used to conduct projectile impact loading experiments. A novel circumferential four probes electrical resistivity method is employed to investigate the damage-sensing capability of hybrid composites. Two different projectile shapes (cone end projectile and stepped cone end projectile) are considered and investigated their effect on the composites’ ballistic limit, impact energy absorption, damage area, and piezo-resistance response. Projectile shape significantly influences ballistic limit and energy absorption, whereas a stepped cone end projectile demonstrates higher amount of energy absorption of about 42% and peak piezo-resistance change of around 60% compared to cone end projectile. The addition of LM improved the ballistic limit by about 20% and the amount of energy absorption by around 50% but reduced damage-sensing sensitivity due to improved electrical conductivity with its presence. Moreover, the intra-ply hybrid composites exhibited lower ballistic limits owing to weaker fiber strength, while inter-ply hybrids showed better energy absorption capabilities, resulting in higher ballistic limits. Thermal imaging technique is adopted in post-mortem analysis of the damaged area, and it revealed delamination inside the intra-ply hybrid composites. 

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3. 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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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