Emerging, hybrid & smart composites

Multifunctional fiber‐reinforced polymer (FRP) composites provide an ideal platform for next‐generation smart composites applications including structural health monitoring, electrical and thermal conductivity, energy storage and harvesting, and electromagnetic interference shielding without compromising their mechanical properties. Recent progress in carbon‐based nanomaterials such as graphene and carbon nanotubes (CNTs) has enabled the development of many novel multifunctional composites with excellent mechanical, electrical, and thermal properties. However, the effective incorporation of such carbon nanomaterials into FRP composites using scalable, high‐speed, and cost‐effective manufacturing without compromising their performance is challenging. This review summarizes the recent progress on graphene and CNT‐based FRP composites, their manufacturing techniques, and their applications in smart composites. Current technical challenges and future perspectives on smart FRP composites research to facilitate an essential step toward moving from research and development‐based smart composites to industrial‐scale mass production are also discussed.

Composites get smart

Bio-based smart composites have rapidly evolved as sustainable alternatives to petroleum-based materials, integrating renewable feedstocks with advanced functionalities. This review analyses recent advancements in bio-based matrices and bio-epoxy reinforced with natural fibres and cellulose nanocrystals, examining their mechanical performance, thermal stability, and environmental benefits. It critically examines the integration of innovative technologies, including embedded sensors, conductive nanofillers, shape-memory polymers, and self-healing systems, with a focus on design principles, actuation capabilities, and practical challenges in composite manufacturing. Manufacturing methods are evaluated for sustainability, process efficiency, and biocompatibility of materials. Life cycle assessment methodologies reveal that bio-based composites can achieve reduction in carbon footprint compared to glass fibre-reinforced plastics when integrated with end-of-life recycling or composting schemes. Nevertheless, persistent challenges remain in moisture resistance, thermal stability, cost-effectiveness, and the standardisation of testing protocols. Future research directions include the 4D printing of stimuli-responsive biocomposites, vitrimer chemistry, integration of AI in design and quality control, and development of policy frameworks for commercial adoption. Applications span robotics, wearable electronics, and smart packaging across multiple industries. Comprehensive synthesis of material innovations, processing technologies, environmental assessments, and regulatory landscapes provides a roadmap for developing sustainable, smart composites in the aerospace, automotive, construction, healthcare, and energy sectors.

A FBG embedded smart composite material, which is capable of measuring temperature and strain beyond the loading bearing missions, has been developed using hot pressing technology. The asymmetric loading induced birefringence which affects the wavelength identification of the embedded FBG has been mitigated by recoating epoxy resin on the grating section. To determine the optimal recoating thickness, finite element simulations have been performed. The simulation results indicate that the recoated epoxy resin can balance the vertical stress and the transversal stress effectively and optimal thickness is around 0.175mm. Additionally, experiments have been conducted to verify the performances of the developed smart composite material. The temperature experiment demonstrates that the smart composite has good linearity in the temperature range of 15–120D. And the static bending test indicates the sensitivities of the embedded FBG with an angle of 0°, 45°and 90°are 33.5pm/kg, 15.7pm/kg and 2.1pm/kg, respectively.

Ferromagnetic shape memory alloys (FSMAs) such as NiMnGa are expected to be new practical actuator materials with high driving frequency by magnetic field and large strain due to the shape memory effect (SME). However, the brittleness and poor workability of FSMAs, especially at a polycrystalline state, are serious problems and should be improved for a practical use. From this viewpoint a smart composite has been designed by a combination of a polymer matrix and FSMA particles (FSMAP), and a systematic investigation has been done for a NiMnGa-FSMAP/epoxy smart composite. This paper summarizes the design concept and some experimental results of the smart composite. It is pointed out that the single-crystal NiMnGa-FSMAP are easily made by mechanical crush due to the brittleness of FSMAs, and microstructural control is also possible by applying magnetic field during curing. Experimental study revealed that the NiMnGa-FSMAP/epoxy smart composites exhibit both tensile ductility and SME, and that shape memory properties become improved by decreasing particle size of FSMAP. It is concluded that the FSMAP/polymer smart composite has a large potential to be a new practical actuator material.

Smart composites with the integration of fiber-optics can be used for temperature sensing as well as stress and strain evaluation. There is a great body of research related to active vibration suppression in smart composites. The technologies reviewed in this chapter have applications in space, aerospace, and land vehicles industries. The technology demonstrated no-touch sensors utilizing conductive paths in preforms and subsequently in molded components. Smart composites have been used successfully in many applications civil engineering structure, satellite positions, etc. The difference here is to incorporate these features in a high-volume manufacturing process in a cost-effective manner. There is a case to make for a safety or a convenience feature in automobile closure panels hoods, doors, decklids, and liftgates to include "no-touch" sensor technology to keep the panels from closing when it senses that there is an interference between the fully open and the fully closed positions.

Herein, a new class of robust bicontinuous elastomer–metal foam composites with highly tunable mechanical stiffness is proposed, fabricated, characterized, and demonstrated. The smart composite is a bicontinuous network of two foams, one metallic made of a low melting point alloy (LMPA) and the other elastomeric made of polydimethylsiloxane (PDMS). The stiffness of the composite can be tuned by inducing phase changes in its LMPA component. Below the melting point of the LMPA, Young's modulus of the smart composites is ≈1 GPa, whereas above the melting point of the LMPA it is ≈1 MPa. Thus, a sharp stiffness change of ≈1000× can be realized through the proposed bicontinuous foam composite structure, which is higher than all available robust smart composites. Effective medium theory is also used to predict the Young's modulus of the bicontinuous smart composites, which generates reasonable agreement with experimentally measured Young's modulus of the smart composites. Finally, the use of these smart materials as a smart joint in a robotic arm is also demonstrated.

Embedding fiber optic sensors in composites can long-term instantly monitor the deformation and damage within the composite structure, which realizes structural health monitoring and life prediction. However, fiber embedding generally brings damage in terms of the composite material integrity and continuity, resulting the extreme stress concentration in the interface of the optical fiber and composite. Therefore, the mechanical properties of the composite material are potentially influenced unfavorably. To investigate the influence of microstructures such as optical fibers on the macroscopic tensile mechanical properties of composites, this paper develops a progressive damage analysis model inspired by the micromechanical failure theory. The established model can predict the stiffness and strength properties of fiber smart composites. The model is further verified by comparing the obtained tensile failure mechanism with the experimental results. The results show that the maximum relative error of the destructive load is only 3%, which demonstrates the accuracy and validity of the model. The work of this paper can provide guides for the optimization and strength prediction of smart composite structures with embedded optical fibers.

Smart and multifunctional fiber reinforced polymer (FRP) composites with energy storage, sensing, and heating capabilities have gained significant interest for automotive, civil, and aerospace applications. However, achieving smart and multifunctional capabilities in an FRP composite while maintaining desired mechanical properties remains challenging. Here, a novel approach for layer‐by‐layer (LBL) deposition of 2D material (graphene and molybdenum disulfide, MoS2)‐based heterostructure onto glass fiber fabric using a highly scalable manufacturing technique at a remarkable speed of ≈150 m min−1 is reported. This process enables the creation of smart textiles with integrated energy storage, sensing, and heating functionalities. This methodology combines gel‐based electrolyte with a vacuum resin infusion technique, resulting in an efficient and stable smart FRP composite with an areal capacitance of up to ≈182 µF cm−2 at 10 mV s−1. The composite exhibits exceptional cyclic stability, maintaining ≈90% capacitance after 1000 cycles. Moreover, the smart composite demonstrates joule heating, reaching from ≈24 to ≈27 °C within 120 s at 25 V. Additionally, the smart composite displays strain sensitivity by altering electrical resistance with longitudinal strain, enabling structural health monitoring. These findings highlight the potential of smart composites for multifunctional applications and provide an important step toward realizing their actual real‐world applications.

A smart composite with functional fibers and reinforcement fibers optimally placed with an embroidery machine was created. Fiber orientation affects mechanical properties of composite laminates significantly. Accordingly, if the fibers can be placed along a desired curved path, fiber reinforced plastic (FRP) structures can be designed more lightly and more sophisticatedly. To this end a tailored fiber placement method using the embroidery machine have been studied. To add functions to the FRP structures, shape memory alloy (SMA) wires were placed as functional fibers. First, for a certain purpose the paths of the reinforcement fibers and the SMA wires were simultaneously optimized in analysis. Next, the reinforcement fibers and tubes with the SMA wires were placed on fabrics by using the embroidery machine and this fabric was impregnated with resin by using the vacuum assisted resin transfer molding method. This smart composite was activated by applying voltage to the SMA wires. Fundamental properties of the smart composite were examined and the feasibility of the proposed creation method was shown.

Smart composites with tunable stress–strain curves are explored in a numerical setting. The macroscopic response of the composite is endowed with tunable characteristics through microscopic constituents which respond to external stimuli by varying their elastic response in a continuous and controllable manner. This dynamic constitutive behavior enables the composite to display characteristics that cannot be attained by any combination of traditional materials. Microscopic adaptation is driven through a repetitive controller which naturally suits the class of applications sought for such composites where loading is cyclic. Performance demonstrations are presented for the overall numerical framework over complex paths in macroscopic stress–strain space. Finally, representative two- and three-dimensional tunable microstructures are addressed by integrating the control approach within a computational environment that is based on the finite element method, thereby demonstrating the viability of designing and analyzing smart composites for realistic applications.

A comprehensive micromechanical model for the analysis of a smart composite piezo-magneto-thermoelastic thin plate with rapidly-varying thickness is developed in the present paper. A rigorous three-dimensional formulation is used as the basis of multiscale asymptotic homogenization. The asymptotic homogenization model is developed using static equilibrium equations and the quasi-static approximation of Maxwell’s equations. The work culminates in the derivation of a set of differential equations and associated boundary conditions. These systems of equations are called unit cell problems and their solution yields such coefficients as the effective elastic, piezoelectric, piezomagnetic, dielectric permittivity and others. Among these coefficients, the so-called product coefficients are also determined which are present in the behavior of the macroscopic composite as a result of the interactions and strain transfer between the various phases but can be absent from the constitutive behavior of some individual phases of the composite material. The model is comprehensive enough to allow calculation of such local fields as mechanical stress, electric displacement and magnetic induction. In part II of this work, the theory is illustrated by means of examples pertaining to thin laminated magnetoelectric plates of uniform thickness and wafer-type smart composite plates with piezoelectric and piezomagnetic constituents. The practical importance of the model lies in the fact that it can be successfully employed to tailor the effective properties of a smart composite plate to the requirements of a particular engineering application by changing certain geometric or material parameters. The results of the model constitute an important refinement over previously established work. Finally, it is shown that in the limiting case of a thin elastic plate of uniform thickness the derived model converges to the familiar classical plate model.

The increasing need for effective Structural Health Monitoring (SHM) systems has accelerated the development of smart cementitious materials with intrinsic sensing capabilities. Among these, self-sensing composites enhanced with carbon nanofibers (CNFs) show significant promise due to their ability to transduce mechanical stimuli into electrical signals. This study presents a hybrid deep learning approach for predicting the fractional change in resistivity (FCR) in self-sensing cementitious composites reinforced, considering the impact of various carbon nanofibers. Building upon previously published experimental results, which systematically evaluated the mechanical, electrical, and piezoresistive properties of CNF-enhanced composites under cyclic compression, two multi-branch neural networks were developed to integrate heterogeneous input modalities. The proposed approach incorporates dynamic loading sequences, static material properties, and formulation-specific categorical inputs processed through dedicated Long Short-Term Memory (LSTM), dense, and embedding layers, respectively. Two model variants were investigated: one relying solely on time-load sequences, and the other incorporating autoregressive FCR information. The dataset was constructed using a sliding window method and partitioned to preserve the distribution of CNF formulations. In the end, a permutation feature importance analysis and ablation study were conducted to quantify the contribution of each input branch and material property to the model performance. Performance evaluation using MAE, RMSE, and R² metrics demonstrated that the model accurately captures the nonlinear resistive behaviour of the composites and generalises effectively across different CNF types and dosages. The results underscore the potential of data-driven frameworks in advancing the predictive capabilities of self-sensing materials for structural health monitoring applications. • Developed hybrid deep learning models to predict FCR in smart composites. • Integrated load sequences, material properties, and CNF types via neural branches. • Compared models with and without autoregressive FCR input for accuracy. • Used sliding window data and feature importance to assess model robustness. • Achieved high prediction accuracy across various CNF types and concentrations.

New self-sensing cementitious composites embedded in the highway pavement for vehicle detection and weigh during motion is fabricated. Smart carbon fiber (CF) reinforced cement-based materials with high sensitive property are used as sensors. These smart composites may be able to detect the traffic and sense the weight in motion, thanks to their piezoresistive property. Cement-based sensors capability to vehicle detection was investigated in a real field at the University of Technology campus. Findings clarify that the CF-Based cementitious composites provide have a great potential to use as sensors for detect traffic and its composition also it possibly identifies different vehicular axle loadings (weigh during motion).

Several easy-to-manufacture designs based on a pair of Fiber Bragg Gratings structure embedded in Carbon Fiber Reinforced Plastic (CFRP) have been explored. These smart composites can be used for strain and temperature discrimination. A Finite Elements Analysis and Matlab software were used to study the mechanical responses and its optical behaviors. The results exhibited different sensitivity and using a matrix method it is possible to compensate the thermal drift in a real application keeping a simple manufacture process.