Showing papers in "Smart Materials and Structures in 2021"

Paper and carbon ink enabled low-cost, eco-friendly, flexible, multifunctional pressure and humidity sensors

Abstract: For a long time, the daily paper and carbon ink have been used for writing and painting. With the development of the electronic technology, they are expected to play new roles in electronic devices. Herein, combining the unique characteristics of the paper (rough surface, hydrophilicity) and carbon ink (conductivity), this work rationally proposed two low-cost, eco-friendly, flexible, multifunctional pressure and humidity sensors. The results show that as-fabricated paper-based (PB) pressure sensor has a good sensitivity of 0.614 kPa−1 in the pressure range of 0–6 kPa. The PB humidity sensor has a large response of ∼2120 (current ratio at 91.5% relative humidity (RH) and 0% RH). The PB pressure sensor is proven to be useful for multiple pressure related contact detections, and the PB humidity sensor can be used for many humidity related non-contact detections. Interestingly, combining the different detecting modes of pressure and humidity sensors, some same detecting functions (e.g. switch, respiratory frequency and speech recognition) are realized from contact to non-contact using PB pressure and humidity sensors, which greatly enhance the wearable comfort. Compared with previous reports, this work demonstrates a much simpler approach without expensive raw materials, toxic reagents and high temperature treatment to achieve outstanding sensing performances of the multifunctional pressure and humidity sensors.

Graded multifunctional piezoelectric metastructures for wideband vibration attenuation and energy harvesting

Abstract: Unlike well-studied locally resonant (LR) metamaterials with a periodic array of identical resonators, "graded" LR metamaterials consist of an array of resonators with a spatially varying parameter, yielding wideband wave attenuation and mode trapping/localization, among other features. In this work, we explore a graded LR piezoelectric metamaterial-based structure (i.e. metastructure) in which the grading parameter, namely the inductive shunt resonant frequency of the unit cells, follows a predefined variation pattern in space (e.g. first-order, quadratic, or fractional). We investigate the effect of such patterns on (i) the vibration attenuation bandwidth, (ii) the localization of vibration modes, and (iii) the harvested power. To this end, we consider a piezoelectric bimorph cantilever hosting an array of piezoelectric unit cells with spatially varying inductive shunts. Fully coupled electromechanical equations describing the metastructure's linear transverse displacement and unit cell voltages are given with a modal analysis framework and solved using the matrix inversion method. The results show that (i) the first-order grading pattern yields the widest bandgap with 65% increase in the bandwidth compared to the standard uniform LR pattern, (ii) the localization of vibration modes follows in shape the corresponding frequency grading pattern, and (iii) the largest power is harvested for the fractional grading pattern. Furthermore, all of the graded resonator configurations result in wider bandwidth in energy harvesting as compared to the uniform resonators case. Overall, the results unveil the fundamental characteristics of this class of graded piezoelectric metastructures and support the design of such multifunctional piezoelectric metastructures for concurrent vibration attenuation and energy harvesting.

EMI-GCN: a hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks

Abstract: Electro-mechanical impedance (EMI) has been proved as an effective non-destructive evaluation indicator in monitoring the looseness of bolted joints. Yet due to the complex electro-mechanical coupling mechanism, EMI-based methods in most cases are considered as qualitative approaches and are only applicable for single-bolt monitoring. These issues limit practical applications of EMI-based methods in industrial and transportation sectors where real-time and reliable monitoring of multiple bolted joints in a localized area is desired. Previous research efforts have integrated various machine learning (ML) algorithms in EMI-based monitoring to enable quantitative diagnosis, but only one-to-one (single sensor single bolt) case was considered, and the EMI–ML integrations are basically unnatural and ingenious by learning the EMI measurements from isolated sensors. This paper presents a novel EMI-based bolt looseness monitoring method incorporating both physical mechanism (acoustic attenuation) and data-driven analysis, by implementing a lead zirconate titanate (PZT) sensor network and a built-in graph convolutional network (GCN) model. The GCN model is constructed in such a way that the structure of the PZT network is fully represented, with the sensor-bolt distance and sweeping frequency encoded in the propagation function. The proposed method takes into account not only the EMI signature but also the relationship between the sensing nodes and the bolted joints and can quantitatively infer the torque loss of multiple bolts through node-level outputs. A proof-of-concept experiment was conducted on a twin-bolt plate, and results show that the proposed method outperforms other baseline models either without a graph network structure or does not consider sensor-bolt distance. The developed hybrid model provides new thinking in interpreting sensor networks which are widely adopted in structural health monitoring, and the approach is expected to be applicable in practical scenarios such as rail insulated joints and aircraft wings where bolt joints are clustered.

Damage detection in large composite stiffened panels based on a novel SHM building block philosophy

Abstract: This paper proposes a structural health monitoring (SHM) building block (BB) approach for guided wave based structural health monitoring (GWSHM) of large composite stiffened panel subjected to varying temperature conditions. The proposed approach follows a similar philosophy to industrial substantiation of composite structures through BB by conducting SHM analysis and associated tests at different levels of structural complexity, beginning with small coupon specimens and progressing through structural elements and details (mono-stringer) and finally sub-components (large stiffened composited panel). Each level builds on the knowledge gained at previous, less complex levels. The detection of multiple barely visible impact damage (BVID) in the large panels is achieved by outlier analysis using a reference pristine database gathered from simple coupons and mono-stringer panels under a wide range of temperature variation. This approach enables the data utilisation from lower-level structure in the proposed BB test scheme to higher-level structures and hence reduce the number of tests required for large and complex structures. The detected damage is then localised by a proposed two-step multi-damage localisation method, which first confirms the presence of the damage and narrows down the damaged section then provides an enhanced damage imaging for precise location estimation. Damage detection with a true positive rate of 0.909 and a false positive rate of 0.111 as well as accurate multiple damage localisation are achieved in the flat fuselage panels for a temperature range of 25 °C–50 °C.

A nonlinear M-shaped tri-directional piezoelectric energy harvester

Abstract: In this paper, we design and experimentally validate a geometric nonlinear M-shaped tri-directional piezoelectric energy harvester. This harvester adopts a single structural design, which consists of a proof mass and a beam bent into an M shape with both ends fixed. Through structural parameter modification, the vibration directions of the first three resonant modes can be nearly orthogonal, which is beneficial to achieve tri-directional energy harvesting. Finite element analysis is performed to guide the design and analyse the working effectiveness of the proposed harvester. The influence of the geometric parameters on the nonlinear stiffness is investigated to broaden the bandwidth of energy harvesting. In the experimental validation, an obvious hardening effect can be observed in vertical excitation at 0.3 m s−2 base acceleration. On the other hand, the power output of the harvester in three directions is in similar level, indicating the capability of tri-directional energy harvesting.

A review of vascular networks for self-healing applications

Abstract: Increasing awareness for sustainability has led to the development of smart self-healing materials, which can extend the service life and improve safety without human intervention Vascular networks are observed in biological systems, such as leaf venation and blood vascular systems, and provide inspiration for self-healing mechanisms in engineered systems Embedding a vascular network in a host material has the advantage of addressing varying magnitudes of damage and allowing for an indefinite replenishment of the healing agent, which are current limitations of intrinsic and capsule-based self-healing systems These networks are demonstrated in polymer and composite materials, with fabrication methods including removal of sacrificial elements, electrospinning, and an array of additive manufacturing (AM) techniques Advances in AM allow more complex network configurations to be realized that optimize fluid distribution and healing potential This review intends to provide a comprehensive overview of the current progress and limitations of the design approaches, fabrication methods, healing mechanisms, and relevant applications of embedded vascular networks Additionally, significant research gaps and future research directions for vascular self-healing materials are described

Lamb wave based damage detection in metallic plates using multi-headed 1-dimensional convolutional neural network

Abstract: Lamb wave based damage diagnosis holds potential for real-time structural health monitoring; however, analysing the Lamb wave response possess challenge due to its complex physics. Data-driven machine learning (ML) algorithms are often more effective in identifying the damage-related features from these complex responses. However, in analysing such complex responses the ML algorithms requires extensive data pre-processing and are often not suitable for real-time damage detection. This paper presents a deep learning multi-headed 1-dimensional convolutional neural network (1D-CNN) architecture capable to operate directly on raw discrete time-domain Lamb wave signals recorded from a thin metallic plate. The multi-headed configuration consisting of two parallel 1D-CNN layers is capable to learn higher order damage-related features and enhances robustness of overall classification performance. To train the adopted 1D-CNN algorithm a diverse database is also constructed consisting 216 numerically and 24 experimentally generated responses of a thin 1.6 mm Al-5052 plate structure. The diversification of training database is achieved by varying parameters like scanning length, scanning frequency and adding different levels of white noises to the captured responses. Later, the trained 1D-CNN architecture is tested against two separated unseen test-databases. The first test database consist of experimentally generated 12 samples with notch-like damage and 12 samples of pristine condition. The proposed 1D-CNN classifier generalizes well on the unseen samples and decisively predicts the outcome for 23 out of 24 samples of first test database. The second test database consists of 108 unseen FE simulated samples capturing additional damage scenarios. In the second test phase, the model has correctly predicted the condition of all the 108 samples.

Effects of installation position of fin-shaped rods on wind-induced vibration and energy harvesting of aeroelastic energy converter

Abstract: The influence of fin-shaped rod (FSR) with different installation positions on wind-induced vibration and energy harvesting of a cylinder-based aeroelastic energy harvester are studied by experiments and simulations. Two FSRs are installed symmetrically on the surface of a circular cylinder, and the coverage angle of each FSR is 20°. The installation position of FSRs on the cylinder is represented by the placement angle, θ, which varies in the range of ±160°. And the tested wind speed range is 0–6.8 m s−1. The results show that FSRs change the position of the separation point of the boundary shear layers, further affect the formation and shedding of vortices. Then the force on the cylinder changes, which causes the energy harvester to produce different vibration responses and energy outputs. When 0° < θ < 70°, back-to-back vortex-induced vibration (VIV) and galloping can be observed for FSR-cylinder, and the output power increases with the increase of wind speed, the maximum output voltage and power reach 18.1 V and 1.645 mW. For 70° ⩽ θ < 120°, the vibration of FSR-cylinder is suppressed, which is not conducive for energy harvesting. When 120° < θ ⩽ 160°, the vibration of FSR-cylinder firstly experiences VIV and then galloping occurs after reaching the critical wind speed.

Size-dependent free vibration analysis of functionally graded porous piezoelectric sandwich nanobeam reinforced with graphene platelets with consideration of flexoelectric effect

Abstract: This paper investigates the effect of flexoelectricity on the vibration responses of functionally graded porous piezoelectric sandwich nanobeam reinforced by graphene platelets (GPLs). The Euler–Bernoulli beam theory and the general modified strain gradient theory are employed to formulate the constitutive equations. Different distributions of porosity and GPLs dispersion patterns are considered and the Halpin–Tsai model is used to predict Young’s modulus and density of the nanobeam. The governing equations and boundary conditions are derived based on the general strain gradient theory and solved by differential quadrature method. A parametric study is accomplished to investigate the effects of flexoelectricity, size-dependence, porosity coefficient, GPLs weight fraction, porosity distributions as well as GPLs dispersion patterns on the fundamental frequency of composite nanobeam. Numerical results indicate that the porosity, GPLs and flexoelectricity can effectively influence the vibration behavior of nanobeam.

Multiphase lattice metamaterials with enhanced mechanical performance

Abstract: We describe here the quasi-static crushing behavior of novel classes of multiphase (hybrid) hierarchical lattice metamaterials. The first class is represented by a hybrid architecture combining a hierarchical honeycomb with polyurethane foam filler, while the second is a multiphase structure produced by injecting an alginate hydrogel into the hierarchical voids of the honeycomb metamaterial. Twelve different auxetic (i.e. negative Poisson’s ratio) and non-auxetic metamaterial architectures have been 3D printed and subjected to edgewise compression crushing loading. A parametric numerical analysis has been also performed using validated finite element models to identify best metamaterial architecture configurations. Configurations filled with the hydrogel showed a significant stabilization of the deformation mechanism during large deformation edgewise compression. The use of metamaterials designs with internal slots and round in the ribs also filled by polyurethane rigid semi-reticulated foam feature however significant increases in terms of specific stiffness, mean crushing force, strength and energy absorption. The enhancement is particularly evident for the hybrid lattice metamaterials auxetic configurations.

Active feedforward control of flexural waves in an Acoustic Black Hole terminated beam

Abstract: Acoustic Black Holes (ABHs) are structural features that are typically realised by introducing a tapering thickness profile into a structure that results in local regions of wave-speed reduction and a corresponding enhancement in the structural damping. In the ideal theoretical case, where the ABH tapers to zero thickness, the wave-speed reaches zero and the wave entering the ABH can be perfectly absorbed. In practical realisations, however, the thickness of the ABH taper and thus the wave-speed remain finite. In this case, to obtain high levels of structural damping, the ABH is typically combined with a passive damping material, such as a viscoelastic layer. This paper investigates the potential performance enhancements that can be achieved by replacing the complementary passive damping material with an Active Vibration Control (AVC) system in a beam-based ABH, thus creating an Active ABH (AABH). The proposed smart structure thus consists of a piezo-electric patch actuator, which is integrated into the ABH taper in place of the passive damping, and a wave-based, feedforward AVC strategy, which aims to minimise the broadband flexural wave reflection coefficient. To evaluate the relative performance of the proposed AABH, an identical AVC strategy is also applied to a beam with a constant thickness termination. It is demonstrated through experimental implementation, that the AABH is able to achieve equivalent broadband performance to the constant thickness beam-based AVC system, but with a lower computational requirement and a lower control effort, thus offering significant practical benefits.

An SECE array of piezoelectric energy harvesting

Abstract: The paper studies the electrical response of an array of piezoelectric oscillators attached to the synchronized electric charge extraction (SECE) interface circuit. The analytic estimate of output power is derived and presented through the matrix formulation of generalized Ohm’s law (charging on capacitance) for the case of parallel (series) connection of energy harvesters. These formulations mainly depend on the proposed equivalent load impedances which are independent of external resistive loads. It therefore offers an advantage of enabling harvested power independent of DC output voltage and making the harvester array desirable for broadband energy scavenging. The proposed framework is subsequently validated both numerically and experimentally. The results show that the power output and bandwidth of an SECE-based array are superior to that based on the standard energy harvesting circuit. Further, it is found that the behavior of an SECE array electrically arranged in parallel connection is different from that connected in series. The former demonstrates the output power higher than the latter, while the latter exhibits roughly uniform peak power in frequency response. However, the experiment indicates the unexpected power drop deviated significantly from the prediction in the array of harvesters connected in series. Such a discrepancy is explained as a result of comparatively serious leakage current in the reverse-biased diodes.

Screen-printed flexible negative temperature coefficient temperature sensor based on polyvinyl chloride/carbon black composites

Abstract: Flexible temperature sensors are needed for real‐time temperature monitoring in healthcare, disease diagnosis, and ambient environment detection. In this work, a flexible negative temperature coefficient (NTC) temperature sensor based on polyvinyl chloride/carbon black (PVC/CB) was screen printed onto a polyethylene terephthalate substrate. The prepared temperature sensor exhibited high sensitivity (−0.148% °C−1), excellent linearity (R 2 = 0.995), a fast response time (0.7 s), and good repeatability when used to measure temperatures between 18 °C and 44 °C. The tunneling effect was used to explain the NTC of the PVC/CB temperature sensor, and its temperature sensing mechanism was proposed. Additionally, the sensor was used to monitor human breathing rates and temperatures, demonstrating its potential for real‐time skin or environmental temperature monitoring.

Early-age strength monitoring of the recycled aggregate concrete using the EMI method

Abstract: At present, the recycled aggregate concrete (RAC) technology has served as an environment-friendly means to dispose the waste concrete. This paper mainly investigates the feasibility of the early-age strength monitoring of the RAC using the electro-mechanical impedance (EMI) method. Both the compressive test and EMI test utilizing the embedded piezoelectric smart aggregate (SA) transducers were conducted on the specimens with different recycled coarse aggregate (RCA) replacement ratios and loading ages. The compressive test results show the early-age strength of the RAC develops more slowly on the whole as the RCA replacement ratio increases. In addition, the EMI test results as well as the theoretical analysis show that the conductance resonant frequency (CRF) of the SA generally rises as the age goes on. By regression analysis, the linear relationship between the early-age strength of the RAC and the CRF increment was established. It validates the applicability of using the EMI method to monitor the early-age strength of the RAC in engineering practice.

Heating and de-icing function in conductive concrete and cement paste with the hybrid addition of carbon nanotubes and graphite products

Abstract: The authors would like to acknowledge financial support received from European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 760940 and from the Generalitat Valenciana (Spain) (AICO/2019/050).

Timber moisture detection using wavelet packet decomposition and convolutional neural network

Abstract: As timber structures are vulnerable to degradation due to the tendency to trap moisture, the present study proposed a new percussion-based method to replace the existing constant contact between structures and sensors. A total of two approaches have been proposed to automated detect the moisture content (MC) of timber: (a) the random forest classifier (machine learning-based) was employed to classify the wavelet packet decomposition (WPD) features extracted from excitation-induced sound signals (WPD + RF); and (b) the 2D-CNN framework (deep learning-based) was employed to classify the Mel frequency cepstral coefficient (MFCC) features extracted from excitation-induced sound signals (MFCC + 2DCNN). The proposed automatic detection methods are covered from 1D time-domain signal classification to 2D image classification. To verify the effectiveness of both two approaches, an experimental study was conducted. The MC of two types of timber specimens (i.e. softwood and hardwood) was gradually increased from 0% to 60% with 10% increments. The change of MC of timber material caused different material properties, resulting in a measurable differential in forced vibration among the various specimens used. The results demonstrated that MFCC + 2DCC outperformed the RF + WPD in MC classification of timber material. Overall, the percussion-based method proposed in this study can provide an outstanding classification performance.