Journal of Intelligent Material Systems and Structures

Traditional device optimization often relies on iterative trial-and-error, a time-consuming and costly approach. Numerical models offer a promising solution to accelerate this process. This work presents a workflow for optimizing existing smart devices using numerical models, with a focus on an innovative torque limiter based on NiTi tapes. A numerical model, calibrated with experimental tensile data, identified critical stress concentration zones in the NiTi tapes during functioning, limiting the cyclic fatigue life of the device. By leveraging the finite element analysis, the geometry was optimized to mitigate these stress concentrations. Additive manufacturing enabled the rapid production of the optimized design. Finally, an experimental validation confirmed the effectiveness of the in silico-driven optimization approach, demonstrating a substantial improvement in the cyclic fatigue resistance of the NiTi tapes.

Electromechanical impedance (EMI) measurements have revolutionized non-destructive evaluation (NDE) of manufactured parts. By bonding piezoelectric elements to structures, EMI analyzes dynamic responses to identify manufacturing anomalies. Particularly effective in additive manufacturing (AM), EMI detects defects like mass alterations and internal porosity. In structural health monitoring, it continuously compares impedance signatures to baseline, detecting emerging defects over time. This innovative approach addresses limitations of conventional methods, providing a powerful tool for assessing material integrity. The practical application of EMI however faces challenges due to the need for direct instrumentation of each part with piezoelectric elements, which introduces time, cost, and variability issues. To address these, the concept of indirect EMI (IEMI) has emerged, utilizing a secondary structure or instrumented fixture to temporarily couple with the part under test. This approach allows for testing multiple specimens, reduces labor requirements, and facilitates process automation. This paper investigates the sensitivity of IEMI to defects in the specimen, focusing on fixture design, instrumentation process, and clamping force calibration. Various interface conditions and clamping forces are explored to understand their impact on defect detection capabilities. Experimental results indicate that interface conditions significantly influence IEMI measurements, with metal-on-metal contact providing the best sensitivity. Additionally, the clamping force is found to affect the impedance signature, emphasizing the need for consistent force application during measurements. Overall, this study underscores the potential of IEMI as a viable NDE solution, highlighting its ability to detect defects in manufactured parts while addressing practical implementation challenges. Future work will focus on optimizing fixture design for enhanced sensitivity.

Magnetorheological elastomers (MREs) are promising smart materials for structural vibration reduction applications due to their variable stiffness with applied magnetic fields. However, when preparing specific MREs for different vibration reduction applications, numerous preparation and testing experiments are generally used to obtain the qualitative relationship between the equivalent stiffness of MREs and the matrix modulus, the volume fraction and size of the magnetic particles, then repeated experiments are performed to finally prepare MREs that have target stiffness, which is time-consuming and inefficient. In this paper, based on the linear elasticity theory, Hooke’s law and chain-like microscopic mechanism, the analytical relationship between the equivalent stiffness of MREs and the matrix modulus, the volume fraction and size of magnetic particles is derived, thus establishing the modified equivalent stiffness model of MREs used for preparation. On this basis, the key preparation parameters, namely the matrix modulus, volume fraction and particle size of magnetic particles, can be directly calculated according to the target stiffness of MREs used in the specific applications, so as to prepare the corresponding samples. Taking the vibration reduction of large power equipment namely enclosed isolated-phased busbar (EIPB) as an example, and based on the calculated parameters, the MRE samples are prepared and tested by mechanical experiments. The results show that when the applied magnetic field strength changes from 0 to 200 mT, the equivalent stiffness of MREs increases from 167 to 312 kN/m, and the relative error is less than 5% when the applied magnetic field strength is above 50 mT. The experimental results show that the proposed equivalent stiffness model has high accuracy, and the preparation method based on the equivalent stiffness model is more practical and can improve the preparation efficiency of specific MREs. The most important contribution of this paper is the utilitarian preparation method, focusing on a theoretical material preparation method. This work has important practical values in the preparation method of MREs for specific applications.

In this paper, an energy harvester based on a lever-type vibration isolator (LVI-EH) for simultaneous low-frequency vibration isolation and energy harvesting is proposed, theoretically investigated, and experimentally verified. The device has a lever structure, mounting springs, and piezoelectric buckled beams. The piezoelectric buckled beams and the lever mechanism play complementary roles: both reduce the isolation frequency, while the lever mechanism amplifies the negative stiffness effect of the piezoelectric buckled beams, enabling a lower isolation frequency. Additionally, utilizing the larger relative displacement at the lever mechanism’s end, the piezoelectric thin films create more strain and stress than those directly mounted on the vibration-isolated object, producing more electrical output. The buckled beam structure with pre-determined deformation ensures that even at the anti-resonance point, larger strains and stresses are still generated, maintaining higher energy output. Through numerical analysis and experiments, we investigated various parameters. Results indicate the proposed LVI-EH device outperforms traditional devices in low-frequency vibration isolation and energy harvesting, effectively tackling issues like low efficiency and narrow working bands.

The deflection of track structures, which represents an abundant source of mechanical energy in railway systems, holds potential for powering monitoring devices. In this study, a kind of displacement-driven piezoelectric energy harvester specifically for steel spring floating slab tracks is developed, which boasts high power output and two notable advantages: (a) Its compact design seamlessly integrates the force transmission metal tube and the piezoelectric stack within the compression spring, significantly reducing the overall height of the device. This makes it ideal for applications where space within track structures is limited. (b) The inverted design of the force transmission metal tube facilitates easy assembly with the high-stiffness compression spring. This design effectively transmits more mechanical vibration energy to the piezoelectric stacks, resulting in substantially greater electrical outputs. Energy harvesting performance of the proposed harvester, including AC voltage and power under the harmonic displacement, has been assessed both experimentally and theoretically. Effects of displacement input, piezoelectric stack parameters and spring stiffness on the performance of the harvester are discussed. In laboratory tests, the fabricated prototype can achieve the maximum average power of up to 942.55 mW at an optimal resistance of 11 kO, under a displacement of 4 mm, and a frequency of 5 Hz. Furthermore, when exposed to the floating slab displacement of steel spring floating slab tracks, the prototyped device was able to harvest a maximum energy of approximately 742 mJ at an optimal resistance of 76 kO, over 10 cycles of floating slab displacement signals. This translates to a total energy of 15.14 J per day, which is sufficient to power the ViPSN2.0 wireless temperature Internet of Things (IoT) sensor node. The findings of this study offer valuable design guidelines for the development of the displacement-driven piezoelectric energy harvesters in railway systems.

Hysteretic response of smart materials has complex mathematical modeling. Thermodynamic-based constitutive models belong to an important class of models and data-driven models are interesting alternatives that avoid complex algorithms and parameter determinations. The classical Preisach model describes multidisciplinary hysteretic behavior employing mathematical operators in a triangular domain. The Everett function is an alternative build a surface from experimental data, replacing the original integral form to a summation. This paper proposes a novel approach, extending the Preisach triangular domain to a prismatic domain that allows a broader description of distinct phenomena. The idea is to use the Preisach approach for different triangles and then performing a interpolation for a prismatic domain, enabling the representation of distinct phenomena that, otherwise would not be described. Shape memory alloys (SMAs) are employed as a representative example of smart materials. Experimental tests are developed in order to define reference cases to be analyzed. Numerical simulations are carried out and compared with experimental data, evaluating the model capabilities under different loading conditions. Specifically, temperature-dependent and cyclic-dependent behaviors are of concern. The results show the model ability to describe the general thermomechanical behavior of shape memory alloy hysteretic behavior, being in close agreement with experimental data.

Passive Structural Health Monitoring (SHM) systems face a challenge in achieving control over the propagating noise field, thereby limiting their application towards defect identification and localization. To overcome this, an outlier statistical technique based on cross-correlation is developed and implemented here to achieve defect detection under passive loading. The proposed technique is first demonstrated using simulation and then validated using an experimental setup based on an air blower as the noise source imposing on an aluminium plate. The results clearly demonstrate the feasibility of defect detection using the sparse sampling-based outlier analysis in a passive SHM system that can effectively assess the structural health integrity of infrastructures deployed under harsh environmental conditions.

Bolted joints are widely used in various sectors due to the ease of connecting two or more parts, but the loss of torque in the bolts of this connection can cause catastrophic failures. This article presents a new piezoresistive sensor composed of a nanocomposite material with rubber, carbon black, and carbon nanotubes designed for monitoring structural health in bolted joints using a new approach for detecting tightening variation. The sensor combines rubber-like flexibility with enhanced conductivity thanks to incorporating carbon nanostructures. The properties of the rubber are thoroughly examined, and the sensor is tested in various scenarios. Its behavior and resistive response are first evaluated under cyclic loading to assess durability and functional reliability. The sensor is also analyzed for its response to environmental effects such as temperature and humidity, and their influence on measurements is evaluated. After that, the sensor is studied as an automatic and static indicator for detecting torque variation when coupled with bolted joints in a structure. This application leverages the sensor’s ability to detect variations in electrical resistance caused by changes in tightening torque, providing a method for identifying such issues. The study indicates the new sensor reliably responds to cyclic loading with resistance variation. Environmental conditions can introduce variations in influence depending on the material’s conductivity level. The sensor was applied to a bolted joint, and the variation in bolt torque can be related to the variation in electrical resistance. In addition, the percentage of nanomaterials makes the sensor more or less sensitive to the torque level, making it possible to produce a sensor for different torque ranges. For the most sensitive sensor, there was a variation of 60% in the measurements after varying by 2 Nm. At the same time, the second, with more nanomaterials, changed the resistance by another 40% for a range of 6 Nm. Furthermore, its application in identifying tightening variations has proven sensitive and effective, successfully detecting torque variations in bolts.

Working continuously in a confined space, the temperature rise of the ultrasonic motor cannot be neglected, and the dynamic performance is sensitive to temperature effects. This research aims to propose a new theoretical model to effectively demonstrate the electric-mechanic-thermal coupling characteristics of the ultrasonic motor. The integration of thermal stress and time-varying parameters related to temperature into the model leads to the establishment of an electric-mechanic-thermal coupled dynamic model. The present model achieves simultaneous evaluation of the mechanical and temperature characteristics of ultrasonic motor. Further experiments validate that the transient characteristics changing of ultrasonic motor can be predicted by the proposed model. The experimental and calculated average errors of motor temperature rise and performance are 1.23% and 2.21%, respectively. This model demonstrates the variation of mechanical characteristics of ultrasonic motor under different temperature fields, and the performance can be controlled to remain basically unchanged by controlling the frequency to decrease by 10 Hz for every 1 °C decrease in temperature. This model can guide the design and control optimization of ultrasonic motor, and enhance the thermal management strategies for ultrasonic motor applications.

Shape memory alloys (SMAs) with shape recovery capabilities have been investigated recently at the European Organization for Nuclear Research (CERN) to develop ring-shaped pipe couplers for vacuum applications. SMA couplers exploit the One-Way and Two-Way Shape Memory Effect (OW- and TW-SME) to mount/dismount vacuum pipe, by temperature variations. A phenomenological analytical model based on simplified elastic-plastic axisymmetric theory has been developed and implemented in a commercial software to simulate biaxial constrained recovery mechanisms in thick-walled SMA rings with rectangular cross section. The model is particularly useful to predict the stress field in the SMA coupler as well as the contact pressure developed at the SMA ring/pipe interface during thermal mounting/dismounting operations, knowing their initial geometry and material properties. The predictions of the analytical model have been compared with experimental data and Finite Element (FE) simulations based on a user-defined material routine.

The performance of miniature mechatronic systems can be significantly improved by incorporating the magnetorheological (MR) fluid-based rotary brake. However, the small-size effect becomes evident in the process of miniaturization. Our observation indicates that a miniature MR brake exhibits a slight eccentricity of 0.43 mm when its rotational axis is runs at a speed of 16,000 rpm. The maximum torque error between compound motion model and calculated dynamic torque based on experimental rotation speed n reached 47.4%, and the average torque error rate is only 8.5%. The paper proposes a comprehensive dynamic model to explain the interaction mechanism between the compound motion and speed reduction performance of a miniature MR brake. A high-speed camera-based dynamic performance testing system is developed to capture the motion state of the shaft and test the speed reduction performance. The dynamic magnetic field distribution under different eccentric distances is calculated by finite element method. The compound motion of rotation and eccentricity is observed when the rotational speed is above 3500 rpm. Results show that the behavior of the miniature MR brake can be well explained by the proposed comprehensive model. At a desired speed of 14,000 rpm, the maximum error of this model (87 rpm) is much smaller than the error of practical models in recent years (1338 rpm). We believe this work is significant for precisely describing the dynamics of miniature MR brake and promoting its application in miniature mechatronic systems.

This paper sets out the motivation for structural power composites: structural materials imbued with the capability to store and deliver electrical energy. The conception and development of structural supercapacitors at Imperial College London is described, with current devices now starting to approach the performance of conventional ‘monofunctional’ composite laminates and supercapacitors. Although these materials could offer tremendous lightweighting and energy storage benefits, there are considerable research challenges yet to be addressed. Melding of composite mechanics and electrochemistry disciplines leads to a daunting research landscape, so the effort has been partitioned into four themes: Constituent Development , Device Assembly and Characterisation , Multifunctional Modelling and Design and Scale-Up and Demonstration . This paper culminates by setting out, for each theme, where future research should focus to advance this exciting technology.

Currently, most piezoelectric energy harvesters designed for the human body are worn on parts such as the arms, legs, and feet. These harvesters primarily rely on continuous motion to generate power. However, the human body is often in a non-moving or static state. Research on energy harvesters that can effectively harvest energy during these static periods is relatively less. In this study, we propose a belt-type low-frequency piezoelectric energy harvester (BLPEH), which converts mechanical energy generated by abdominal expansion or chest movement in the stationary state into electrical energy via piezoelectric stacks. A deformable force amplification mechanism (DFAM) is designed, enhancing the energy harvesting efficiency while ensuring comfort in wear. Experiments demonstrate that under the condition of a stationary standing human body, the BLPEH with a single piezoelectric stack generates an open-circuit voltage of 2.674 V and can achieve an average output power of 2.64 µW. Without any energy storage components, the BLPEH can directly illuminate six LED lights. Overall, this design may not only address the energy supply problem for wearable electronic devices but also provide a new method for harvesting human energy.

In the establishment of intelligent transportation systems, traffic markings, often serving as surface guidance signs on roads, are facing an urgent need for functional attribute upgrades and innovation. Hence, this study involved embedded piezoelectric film transducers (PFTs) into traffic markings to endow them with the capabilities of energy harvesting and signal transmission. A layered preparation method for protecting the PFTs and optimizing burial depth was developed and validated. The effects of load levels, vibration frequencies, and burial depths on the open-circuit voltage and current responses were systematically investigated. Long-term vibration and high-temperature exposure were applied to the traffic markings with embedded piezoelectric thin film transducers. The results indicated that the layered preparation method effectively protected the piezoelectric film and controlled burial depth. The vibration frequency was directly proportional to the electrical response, while the burial depth was inversely proportional to the electrical response. The maximum voltage reached 9.2 V and the power attained 8.6 mW under lab-based rolling mode. And the maximum voltage reached 6.4 V under lab-based vibration mode. The open-circuit voltage doubled after prolonged vibration of 5 million cycles. A high temperature of 60°C resulted in a 30% reduction in open-circuit voltage.

Cellular membranes, nature’s original multifunctional composite material, play key roles in sensing, actuation, energy storage and conversion, communication, and even computing in living creatures. These capabilities stem from their fluid architectures replete with functional biomolecules. Our research has worked to advance the assembly, characterization and use of fluid, biomimetic membranes as bio-inspired platforms for stimuli-responsive and adaptive materials. This article briefly reviews our work to develop biomimetic membranes and their applications, including as artificial synapses and neurons in low-power neuromorphic computing.

State-of-the-art teleoperation systems are installed in various specialized robots, such as disaster, underwater, and surgical robots. However, the sensitive nature of their tasks causes psychological stress in operators. To reduce this stress, bilateral controllers are used to achieve a force feedback function that can support operational skills with gravity/friction compensation and haptic perception. Therefore, we developed several types of haptic devices with magnetorheological (MR) fluids as the core technology for fine haptics. MR fluids are composite materials comprising ferromagnetic particles, medium oils, and several types of additives. Their rheological properties change rapidly, stably, and repeatedly with the application of an external magnetic field. In our previous study, we developed desktop and standalone MR fluid (MRF)-based haptic devices for surgical simulations. However, handheld devices have not yet been developed. Therefore, we designed and developed a compact MR fluid device with a total mass and maximum torque of approximately 100 g and 0.3 Nm, respectively. Static, step, hysteresis, and repeat tests were conducted for the 0.3 Nm-class MRF device. According to the results of the repeat tests, the variation coefficient was 0.5% for a current input of 0.5 A, demonstrating the suitability of the developed device as a fine haptic generator.

Accurate load identification is a prerequisite for monitoring damage processes such as fatigue accumulation. This work approaches the load recognition problem in an inverse inferential setting by processing readings from a strain sensor grid mounted on the operating wind turbine rotor blade. The aeroelastic pressure field is considered an equivalent lumped load vector applied at given stations along the blade’s length, and its magnitude is the quantity of inferential interest. The technical challenge of optical sensor placement is addressed through D-optimal designs that promise sensor architectures, that is, locations and features, which offer a minimal uncertainty propagation of the sensor readings to the load inferences. Synthetic data are generated through finite element simulations based on an actual composite material geometry to demonstrate and quantitatively assess the effectiveness of the process. D-optimal sensor grid designs are obtained through the employment of Genetic Algorithms. Further reduction of the involved epistemic uncertainty due to the problem’s inherent ill-conditioning is assessed by evaluating sensor grids with increasing sensor numbers. The proposed inferential scheme presents a robust way to approach the inverse load identification problem.

Structural batteries refer to multifunctional composite materials capable of storing and delivering electrical energy while carrying mechanical loads. One of the most promising structural battery concepts demonstrated to date utilises carbon fibres in both electrodes, providing combined high electrochemical capacity, electric conductivity and elastic modulus and strength. This paper reports on the state-of-the-art for all-carbon fibre based structural batteries.

The current paper discusses the use of magnetorheological fluids (MRFs) for the lubrication of journal bearings as an effective solution for controlling performance-wise a variety of rotor bearing systems applying an external magnetic field. To account for the magnetic flux production in the lubricating layer, a 2D axisymmetric magnetostatic model is developed while the static performance metrics of the journal bearing are assessed via a 3D CFD analysis modeling the MR fluid as a Bingham plastic, the rheological parameters of which depend on magnetic field intensity. Moreover, is concluded that MRFs have a wear compensatory feature, increasing the minimum lubricant thickness to a degree that completely compensates possible wear, filling the worn region with high viscosity lubricant.

In order to solve the high-frequency vibration problem of industrial pipeline in petrochemical enterprises, a tuned mass damper (TMD) based on magnetorheological (MR) technology was proposed. The magnetorheological-tuned mass damper (MR-TMD) can reduce the vibration response of the structure by tuning resonance with the pipeline system, it can also have the semi-active control characteristics of MR devices. By analyzing the vibration signals collected on the pipeline site, the relevant design requirements and parameters of the damper were determined. The optimal frequency ratio, passive damping, and mass ratio of the damper were determined using the optimal design theory. Meanwhile, the high-performance MR composite was prepared for the working conditions of MR-TMD and a modified Herschle-Bulkley model was derived to characterize the behavior of the material. Furthermore, the mechanical performance of the proposed MR-TMD was tested, the output damping force is about 890.5 N when the applied current is 2 A, which can meet the working requirements of pipeline vibration attenuation. Then, according to the established vibration control model of pipeline system, the vibration system was simulated and analyzed combined with LQR algorithm, and the vibration attenuation performance in different conditions were predicted and compared. The research shows that the TMD combined with MR technology can effectively reduce the vibration of industrial pipeline system.

Vibration control remains a pivotal challenge in engineering, demanding solutions that offer wideband and low-frequency effectiveness with minimal energy. This paper introduces a hybrid vibration control method, combining semi-active piezoelectric (Piezo), and passive acoustic black hole (ABH) techniques. The semi-active approach, similar to Bang-Bang control, efficiently curtails low-frequency vibrations without precise control modes, using scant external energy. Concurrently, passive strategies exploit the ABH effect to absorb medium-high frequency waves through focused damping. The integration of Piezoelectric integrated ABH (Piezo-ABH) structures showcases the feasibility of broad-spectrum vibration dampening, with experimental outcomes revealing significant vibration reduction across 20 ∼ 2000 Hz, averaging an 8.0 dB decrease.

Magneto-Rheological fluid (MRF), known for changing properties under a magnetic field, is ideal for brakes and dampers in magnetically controlled devices. This research presents a novel design for a 10-disc MR brake using in-house Magneto-Rheological Fluid (MRF), distinguished by its integration of electromagnet windings directly onto the brake shaft. Magneto-static analysis, performed using Finite Element Method Magnetics (FEMM) software, optimized the material selection and dimensions, enhancing the magnetic field distribution across the MRF gap and maximizing braking torque. The design, with rotor windings and a consistent MRF gap, generates a uniform magnetic field, significantly boosting performance. Theoretical braking torque was estimated using Bingham plastic model for MRF characterization, aligning well with experimental results. The compact 10-disc MR brake design, weighing 1.19 kg, shows robust torque performance across varying current levels. Remarkably, prior research had not integrated electromagnet windings directly on the rotor of MR brake, marking this study as pioneering in advancing MR brake performance.

The application of 2-2 cement-based piezoelectric transducers (2-2) in the field of structural health monitoring (SHM) encounters obstacles due to temperature effects. To address this issue, this study investigates the effect of temperature on the electro-mechanical admittance (EMA) spectrum of the 2-2 and piezoelectric smart aggregates (SA), employing both theoretical and experimental approaches. Initially, the admittance temperature effect was scrutinized based on the 2-2 theory. Subsequently, experiments were conducted to assess the admittance temperature effects of the 2-2 and SA. Finally, both the 2-2 and SA were embedded in cement mortar specimens, and the admittance temperature effect was analyzed. Experimental results reveal that temperature variation induced distinct behaviors in the EMA characteristics of 2-2 and SA in low and high frequency bands. Theoretical investigations and experimental results demonstrate a certain degree of consistency. When the sensors were embedded within the cement specimens, its EMA spectrum and the temperature sensitivity of the EMA change, particularly evident in the conductance peaks. This should be taken into consideration in future work involving SHM using the 2-2 sensors.

In terms of vibration and noise reduction for helicopter, Active Control Flap (ACF) rotor technology, leveraging smart materials, stands out as a promising and advantageous approach. This paper focuses on the design, modeling, and simulation of a novel structure integrated with trailing-edge flap and composite rotor blade driven by Macro Fiber Composite (MFC) actuators. A 3D model is employed to simulate the deformation response of the flap under different driving voltage levels. The results were validated by experimental data. Additionally, Fluid-Structure Interaction (FSI) analysis is applied to explore the deflections of the trailing-edge flap under various flight conditions and its corresponding aerodynamic characteristics. The findings reveal that the designed trailing-edge flap significantly influences the aerodynamic lift and pitch moment of the airfoil at operational speed and angle of attack of the helicopter blade. Finally, a Back Propagation (BP) Neural Network is introduced to establish a fast predictive model for the intricate nonlinear response characteristics of the ACF rotor. The network is trained and tested with appropriately chosen sample data, demonstrating high prediction accuracy and reliability. This model serves as a theoretical reference for subsequent application of ACF technology in vibration and noise reduction, providing valuable insights for further research and development.

Accurate diagnosis of crack size is a critical task for guided wave (GW)-based structural health monitoring (SHM). However, fatigue cracks would have complex morphology due to complex structural geometries and loading conditions, in which multiple dimension characteristics, like crack length, depth, and angle are involved. It is challenging to quantitatively evaluate these characteristics with GW signals from a single excitation-sensing path. This paper proposes a novel deep guided wave convolution neural network (CNN) committee-based multi-path GW fusion diagnosis method, aiming at quantitative evaluation of dimension characteristics of the complex fatigue damage. GW signals from multiple excitation-sensing paths are synthesized as a high-dimension input image to enhance the effects of the fatigue crack. Besides, the deep GW-CNN committee is developed for damage quantification, in which each GW-CNN is trained with a portion of the training dataset to reduce the impact of small sample size. The proposed method is validated on fatigue tests of landing gear beam specimens under variable amplitude loading, which is designed referring to the critical region of a real aircraft and its fatigue crack presents as a corner crack. The leave-one-out validation results show the effectiveness of the proposed method, especially improvements in the diagnosis of small cracks.