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Aramid nanofibers (ANFs) are a strong and heat‐resistant nanomaterial that can be isolated from commercial para‐aramid fibers, which allow a bottom‐up self‐assembly to form ordered macroscale structures like ANF films. However, the anisotropic nature of high aspect ratio ANFs is not fully exploited when fabricating ANF films for the optimal mechanical properties. In this research, direct ink writing (DIW) is applied to produce ANF‐assembled films with arbitrary shapes, and the shear‐induced alignment of ANFs can follow the printing path direction. Therefore, controlled alignment of ANFs following the computer‐programmed printing pattern is achieved by DIW, which provides a path for the application of topology and nanofiber alignment optimization in nanofiber‐assembled films. In addition, the resulting DIW ANF films exhibit outstanding Young's modulus of 8.39 GPa, tensile strength of 198 MPa, and tensile toughness of 19.4 MJ m⁻³in the alignment direction, together with a wide working temperature range up to 440 °C without losing 50% of its room temperature storage modulus. Moreover, the demonstrated self‐joining ability, rollability, and lamination processability of the DIW ANF films expand their potential applications toward high‐temperature ultrathin tubes, substrates for flexible printed circuit boards, and three‐dimensional all‐ANF lightweight structural parts in extreme environments.

 

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. 

The continuous monitoring of strain in fiber-reinforced composites while in service typically requires bonding a network of sensors to the surface of the composite structure. To eliminate such needs, and to reduce bulk and limit additional weight, this work utilizes the transfer printing of laser induced graphene (LIG) strain gauges onto the surface of commercial fiberglass prepreg for the in situ self-sensing of strain. The resultant embedded strain sensor is entirely integrated within the final composite material, therefore reducing weight and eliminating limitations due to external bonding compared to current alternatives. Additionally, the simple printing process used here allows for the customization of the size and sensing requirements for various applications. The LIG strain sensor is shown to be capable of tracking monotonic cyclic strain as shown during tensile loading and unloading of the host composite, while also proving capable of tracking the dynamic motion of the composite which is characterized via frequency response and sinusoidal base excitation. The LIG strain gauge in this work can thus be used for tracking either quasi-static or dynamic variations in strain for the determination of the deformation experienced by the material, as well as the frequency content of the material for structural health monitoring purposes. 

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. 

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. 

Aramid fiber reinforced polymer composites have been shown to exhibit impressive mechanical properties, including high strength-to-weight ratio, excellent abrasion resistance, and exceptional ballistic performance. For these reasons, aramid composites have been heavily used in high impact loading environments where ballistic properties are vital. In-situ damage monitoring of aramid composites under dynamic loading conditions typically requires externally bonded sensors, which add bulk and are limited by size and space constraints. To overcome these limitations, this work presents a piezoresistive laser induced graphene (LIG) interface for embedded impact sensing in aramid fiber reinforced composites. Through the monitoring of electrical impedance during ballistic impact, information regarding time and severity of the impact is obtained. The impact velocity correlates with the impedance change of the composites, due to delamination between aramid plies and damage to the LIG interface. The delamination length in Mode I specimens also correlates to changes in electrical impedance of the composite. The interlaminar fracture toughness and areal-density-specific V50 of the LIG aramid composites increased relative to untreated aramid composites. This work demonstrates a methodology to form multifunctional aramid-based composites with a LIG interface that provides both improved toughness and imbedded sensing of impact and damage severity during ballistic impact.