Conventional positioning systems for non-destructive testing (NDT) are often bulky, inaccurate, and constrained in motion range, making them unsuitable for inspecting small, intricate components like chips or for long-distance scans. To overcome these limitations, we propose a novel miniature traveling-wave piezoelectric actuator with high positioning performance. The actuator features a simple cross-shaped structure, driven by dual piezoelectric elements to generate a traveling wave. Characterization demonstrates a no-load speed of 350 mm/s, a load capacity (>50 g) exceeding seven times its own weight, excellent linearity (close to 1), repeatability (>90%), unlimited stroke, and high motion resolution (0.023 mm). When integrated with an ultrasonic NDT probe, the system successfully performed both 2D and 3D scanning tasks, overcoming the constraints of conventional systems. This work extends the application of piezoelectric actuators to NDT and provides a viable pathway toward miniaturized, high-performance inspection systems.
McKibben Artificial Muscles (AMs) are widely used in soft robotics as actuators due to their compliance and high force output. However, achieving complex motions with multiple AMs in different orientations remains challenging, as each requires a bulky pneumatic or hydraulic system, complicating assembly and increasing the risk of leaks. These limitations restrict their use in compact or lightweight applications. Inspired by natural muscle architectures that optimize force distribution and enable multidirectional motion, this study aims to integrate complex fiber arrangements into a single McKibben AM. A novel fabrication technique was developed using an interplay between multiple traditional Japanese Kumihimo braiding disks to create multi-directional AMs. Two case studies demonstrate the approach: (1) a bending actuator capable of achieving programed bending angles depending on actuation pressure and (2) a convergent actuator whose combined branches reconfigured output force direction to extend motion range. The resulting branched AMs locally encode complex motion within their structure, eliminating the need for complex actuator systems. This manufacturing strategy advances McKibben AM design toward more compact, adaptive and versatile systems for advanced soft robotic applications.
As the strong nonlinearity and highly complex of piezoelectric energy harvesters (PEH), accurately predicting the dynamic response using mathematical models remains challenging. This study introduces a CNN-LSTM (Convolutional Neural Network, Long Short Term Memory) combined with a neural optimization machine (NOM) to predict the nonlinear dynamics of the PEH. CNN extracts local patterns from inputs related to incentives, while LSTM layer captures their temporal evolution. NOM uses a differentiable surrogate model to automatically optimize the hyperparameters of the CNN-LSTM, thereby improving the predicting accuracy. The proposed framework avoids explicit dynamic modeling, computationally numerical simulation, and repetitive parameter identification. The framework was then applied to predict the dynamic responses (including tip displacement, adaptive rotation angle and output voltage) of a nonlinear, directional self-adaptive PEH, and the results showed that the determination coefficients ( R 2 ) of all three responses exceeded 0.998, and the root mean square errors (RMSE) of tip displacement, adaptive rotation angle, and output voltage were 0.1156 mm, 0.2736°, and 0.2313 V respectively. There was good consistency between the predicted and measured responses, validating the effectiveness of the proposed method and opening up opportunities for accurately predicting the dynamic characteristics of highly complex and/or strongly nonlinear energy harvesting systems.
Two crucial challenges faced in the design of ocean wave energy converters (WECs) are maintaining high capacity factors and remaining robust in everchanging, harsh ocean environments. This study introduces a novel mechanism for achieving reactive WEC control with a soft hydraulic power take-off, thereby addressing both challenges. The device of interest involves a hydraulic pump, constructed from a fluidic flexible matrix composite (F2MC), which is cyclically stretched by driving waves and pressurizes working fluid through a turbine-generator. This class of fiber-reinforced stretch hoses is established for ocean use as robust moorings and shock absorbers. The dynamic properties of the F2MC pump depend on internal pressure, which can be controlled by a continuously variable transmission between the turbine and generator, allowing the system to be adapted for better performance over a wide range of wave conditions. This work presents a dynamic model for operation and adaptive behavior of this power take-off system, experimentally validates components of the model, and performs dynamic simulations of the system that show increases in power production and capacity factor compared to non-adaptive operation for a variety of driving wave conditions. This work develops the basis for future large-scale experimental testing and controller design of this system.
Materials that exhibit self-sensing via the piezoresistive effect (i.e. having strain-dependent electrical properties) have been massively explored in diverse and far-reaching potential use cases including civil and aeronautical structural sensing, biomedical and human health monitoring, consumer products (e.g. tactile sensors), and, among many other examples, robotics. In all of these applications, however, users of self-sensing materials are typically not directly interested in the electrical state of the material. Rather, they want to know the underlying strain state that gives rise to an observed electrical change. Deducing strain requires a model of the piezoresistive effect—a model of the coupling between electrical conductivity (or resistivity) and strain. Existent modeling approaches for piezoresistivity are diverse, including microscale modeling of the interactions among individual conductive fillers and homogenization approaches capable of making macroscale predictions. This review article therefore has two goals: First, we summarize prevailing modeling approaches, including methods, observations, and limitations. And second, we identify key gaps in the state of the art that remain to be closed, especially with an eye toward the goal of using models in practice to characterize self-sensing effects. A brief summary of the physics of self-sensing is also provided.
In this article, a tunable nonlinear energy sink (TNES) is developed for simultaneous vibration suppression (VS) and energy harvesting (EH) over a wide frequency band. The TNES consists of an S-shaped pinned-pinned beam carrying a pair of oscillating magnets at its midpoint. These magnets interact with four tuning magnets mounted on the primary structure to form a tunable magnetic spring. As the oscillating magnets move, they pass through a pair of coils rigidly fixed to the base, functioning as dual electromagnetic energy harvesters (EMEHs). Because the coils are fixed to the base rather than attached to the primary mass, and are connected in series to a resistive load, the EMEHs serve as a grounded electromagnetic damper. By adjusting the spatial positions of the tuning magnets, the TNES can be configured to exhibit mono-stable, bi-stable, or tri-stable behavior. A systematic methodology is proposed to tune the TNES into an optimal mono-stable nonlinear energy sink (MSNES) that closely emulates an ideal NES. A multi-objective optimization is conducted to investigate the trade-off between VS and EH with regard to initial displacement and load resistance. The results reveal that with a set of appropriately selected variables, the optimal MSNES is able to simultaneously achieve a performance index of 62.37% in VS and 32.37% in EH. The transient VS and EH performance of the optimal MSNES is examined through numerical simulation and experimental validation. The results show strong agreement, confirming the effectiveness of the proposed MSNES in delivering broadband vibration mitigation and energy harvesting.
This work investigates the compressive stiffness behavior of magneto-active elastomer (MAE) beams and plates under quasi-static axial loading and externally applied magnetic fields. We employ two- and three-dimensional finite element multiphysics simulations to characterize buckling responses and stiffness variations, as motivated by aerospace applications requiring adaptive stiffness and morphing capabilities. The study examines the influence of geometry, magnetic programming, temperature, and the applied magnetic field on pre- and post-buckling stiffness. These parameters are modeled for beams and plates using COMSOL Multiphysics simulation software. Validation against analytical predictions and prior literature confirms model accuracy. For beams, results show that magnetic programming and field strength can change buckling mode shapes, reduce critical loads by at least 20%, and alter stiffness ratios by factors exceeding 1000, enabling dramatic stiffness tailoring. Plates, in contrast, show much lower difference in stiffness ratio with factors less than 2, and also demonstrate that out-of-plane displacement increases under increased temperature for combined thermal and magnetic inputs. These findings highlight the potential of MAE-actuated buckling structures for aerospace components or bioengineering systems.
Nitinol alloy is one of the most interesting options for the manufacture of biomedical devices, due to its superelastic behavior, which is particularly exploited for the endovascular self-expanding stents. This work intends to exploit the Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM) technique to overcome the main limitation of the NiTi: it is available only in wires or tubes and needs laser cutting to be shaped as a stent. By using LPBF, not only tubular stents can be manufactured, but other shapes can also be envisioned to treat pathologies in vessel bifurcations or in vessels with variable diameters. The work includes a dedicated experimental campaign with metallographic analyses to evaluate microstructure and defects, differential scanning calorimetry (DSC) to determine transformation temperatures, and tensile tests to assess the mechanical performance of LPBF printed coupons. Finally, a stent-like demonstrator is fabricated and chemically etched to demonstrate the proposed manufacturing route. The findings provide insight into the process parameters required to manufacture stent-like NiTi structures by 3D printing, establishing a process chain and dimensional control approach capable of producing geometries within the range reported for commercial .peripheral self-expanding stents. In this respect, the present process validation constitutes a necessary foundation for the subsequent optimization of thermal post-processing aimed at restoring appropriate transformation temperatures and functional behavior. The work therefore supports the future development of patient-specific NiTi stents, provided that the required heat-treatment route is successfully established.
This study systematically evaluates mixing-rule models for predicting the effective magnetic permeability of magnetorheological fluids (MRFs) from weak-field susceptibility measurements. Carbonyl iron powder suspensions in glycerol were prepared over 14 particle volume fractions, and their initial magnetic susceptibilities were measured experimentally. Additional measurements and validation tests were used to assess the robustness of the proposed empirical formulation beyond the primary fitted dataset. Nineteen classical and semi-empirical mixing rules were compared under identical experimental conditions. The results show that models neglecting concentration-dependent demagnetization effects may reproduce the overall monotonic trend but still produce systematic residual deviations across the investigated concentration range. Based on the Ollendorff and Gregorev–Kirko approaches, we propose an empirical OGK formulation that extends demagnetization-based mixing rules by introducing a concentration-dependent effective demagnetization correction. The OGK formulation is intended for low-field susceptibility/permeability data and should not be interpreted as a universal first-principles constitutive law. The three-parameter OGK 3 model is treated as the parsimonious baseline, whereas the four-parameter OGK 4 extension is retained only when supported by residual behavior, information criteria, cross-validation, and validation data. Within the investigated systems, this framework improves the representation of μ eff and of the inferred N eff , while requiring system-specific calibration.
Recent advances in machine learning have seen a wide range of applications across many fields. When combined with developments in flexible, skin-interfaced pressure sensors, these technologies are driving a new generation of personalized health monitoring. From preventing diabetic foot ulcers to tracking respiratory rate and other vital signs, these systems are advancing smarter and more responsive healthcare solutions. This review presents a comprehensive overview of the latest developments in skin-interfaced flexible pressure sensing, starting from their physical mechanisms and ending at microscale material structure. In addition, advanced machine learning approaches for sensor data processing and interpretation is explored, ranging from the fundamental concepts to more recent deep learning models such as temporal convolutional networks for time series classification. Moreover, a systematic review of recent literature is presented, highlighting the application of machine learning in analyzing signals from flexible pressure sensors. Emerging applications leveraging machine learning techniques to facilitate smart health monitoring and human-machine interfaces are explored. A concluding section outlines the challenges and outlook for these emerging technologies as it relates to the biomedical field. To the best of our knowledge, this is the first review which evaluates the potential integration between skin-interfaced flexible pressure sensors with cutting-edge machine learning models, offering a synergistic perspective on next-generation biomedical applications.
The utilization of nanocarbon black (nCB) in cementitious composites has shown significant potential for developing self-sensing concrete with intrinsic strain monitoring and damage detection capability. While nCB with different morphologies and surface areas have been used to produce self-sensing concrete, there is a considerable knowledge gap in understanding how the structure of nCB influences the performance of these smart materials. In this study, the effect of nCB structure on the mechanical and piezoresistive properties of nCB-based self-sensing concrete is investigated. The investigation concludes that the compressive strengths of all the nCB-based composites, regardless of nCB structure, show similar trends: as nCB dosage increases, strength initially increases compared to plain concrete, and then decreases. On the other hand, the composite with high-structure nCB, which has a branched morphology, possesses a very low and distinct percolation zone, beneficial to realizing large-scale implementation due to cost-efficiency. Moreover, it exhibits the highest strain sensitivity as well as highly repeatable and precisely synchronized response to the applied compressive stress-strain. The outcomes of this study shall contribute to the foundational knowledge base for the development of next-generation smart infrastructure, enabling real-time structural health monitoring to enhance safety and service life.
The magnetic properties of elastomers based on silicone matrix and iron microparticles assembled in linear chain-like aggregates of different length are experimentally investigated. For this purpose, elastomer samples with a low concentration of magnetic filler are structured in a magnetic field of various strength. The influence of the particle aggregates morphological characteristics on the macroscopic magnetic response of the samples is revealed. Moreover, the influence of the angle between the direction of particle aggregates, that is, the field applied in the process of crosslinking the polymer matrix, and the direction of the field applied during magnetic measurements on the macroscopic magnetic properties of the composite is taken into account. Magnetic measurements are supported by the evaluation of the real microstructure of the samples using X-ray computed microtomography and corresponding digital image processing methods.
This paper proposes a Y-shaped bifurcated beam magnetic self-coupled piezoelectric energy harvester (Y-MSPEH). By incorporating built-in permanent magnets, the harvester is capable of nonlinear stiffness adjustment and broadband energy harvesting, which in turn helps reduce its physical footprint. To this end, a dynamic model of the Y-MSPEH is developed based on Hamilton’s principle, with geometric nonlinearity and piezoelectric coupling effects taken into account. The study derives numerical solutions for the system response through theoretical analysis and validates them via experiments. For the Y-MSPEH, key parameters, including the magnet’s terminal length, branch angle, and magnetic field strength, are analyzed under both repulsive and attractive magnetically self-coupled states. The results show that adjusting the parameters of the Y-MSPEH can effectively tune the peak frequency of the output voltage response and broaden the response frequency band. Comparative studies indicate that the attractive and repulsive magnetically self-coupled states result in wider response bandwidth and higher response amplitude, respectively. Specifically, compared with the linear Y-shaped bifurcated beam harvester and the geometrically nonlinear Y-shaped bifurcated beam harvester, the Y-MSPEH achieves an approximately 8.26% increase in effective bandwidth in the magnetically self-coupled attractive state, thus demonstrating superior energy harvesting performance.
Dielectric elastomer transducers (DETs) are used in various applications, ranging from actuators to sensors and generators. For optimal performance, homogeneous electrical charging characteristics, which depend on the current flow distribution over the electrode area, are indispensable. To enable the investigation of such current flow distributions, a novel approach using thermal imaging (TI) is presented in this paper. The approach uses high-frequency voltage excitation to induce resistive heating in the DET electrodes and generates temperature maps to visualize the current flow, thus providing insight into the local charging behavior. This way, the effects of electrode geometry, electrical contact layout as well as potential manufacturing-related imperfections can be studied. The paper starts by introducing an experimental setup and the effect of excitation parameters. It subsequently illustrates the method using 50 & micro;m strip-shaped silicone films with different screen-printed carbon black/PDMS electrodes, also taking the effect of mechanical strains into account. The results allow for an interpretation of the effectiveness of electrode design, motivate potential future quality tests and may serve as a validation method for, for example, multi-physics finite element simulation tools.
The cold sintering process (CSP) and self-healing ceramics represent two transformative strategies for addressing the long-standing challenges of energy-intensive processing and brittle failure in ceramics. CSP achieves densification at 120 degrees C-300 degrees C using transient solvents and pressure, enabling the integration of temperature-sensitive phases while reducing embodied energy. Self-healing ceramics restore structural and functional integrity through intrinsic oxidation or embedded healing agents, extending service lifetimes. This Perspective highlights the synergistic integration of these approaches. We propose CSP as both a fabrication route for hybrid healing architectures and a novel in-field repair technique for damaged ceramics. The opportunities include co-processing of ceramics with polymers, low-melting glasses, and microcapsules that are incompatible with conventional sintering. Critical challenges remain, including solvent-agent compatibility, activation energy mismatch, and balancing healing efficiency with mechanical strength. A forward-looking roadmap is outlined, emphasizing scalable CSP platforms, multiscale modeling, and lifecycle assessment. By linking processing science, materials chemistry, and sustainability metrics, we argue that CSP-enabled self-healing ceramics offer a pathway toward intelligent, damage-tolerant materials aligned with green manufacturing and circular economy goals.
Cyclic super elasticity in shape memory alloys (SMAs) is governed by complex transformation mechanisms that evolve under repeated loading, yet these stabilization effects remain insufficiently resolved at the full-field scale. This work presents an experimental investigation of the cyclic superelastic response of Nitinol subjected to 77 loading cycles, with particular emphasis on the progression of stress-induced martensitic transformation. High-resolution digital image correlation (DIC) was employed to capture in-situ strain localization and the corresponding evolution of transformation morphology. The full-field measurements reveal distinct changes in transformation band formation, propagation, and recovery as cyclic stabilization develops. Local strain evolution was further analyzed to elucidate micromechanical deformation pathways that influence functional fatigue behavior. The results provide new insight into the spatially heterogeneous deformation characteristics of Nitinol under cyclic superelastic loading, offering experimentally grounded evidence critical for advancing intelligent material systems and their reliability in repetitive actuation or structural applications.
Steer-by-Wire (SbW) systems eliminate the mechanical linkage between the handwheel and roadwheels, necessitating artificial tactile feedback to restore steering feel. This paper focuses on the design, optimization, and experimental validation of a compact magneto-rheological torque feedback device (MRTFD) intended for SbW applications. To accommodate the elongated axial geometry and severely limited radial envelope of SbW steering columns, an axially oriented comb-shaped MRF channel configuration is developed. This architecture enhances magnetic field utilization and load-carrying capability while maintaining a low off-state torque, enabling a wide controllable dynamic range within stringent geometric constraints. A constrained multi-objective optimization framework is employed to balance dynamic torque range and energy consumption under a prescribed maximum activated feedback torque constraint. The optimized design is subsequently fabricated and experimentally characterized, showing close agreement between measured and simulated torque responses. Furthermore, the prototype is integrated into an SbW steering test rig and evaluated under a feedback control scheme that combines a PID controller with inverse-model feedforward compensation. Experimental results demonstrate accurate torque tracking over various steering rates, thereby highlighting the feasibility and effectiveness of the proposed MRTFD for future SbW steering feedback applications.
As researchers continue to develop morphing aerospace structures capable of changing shape in real time to adapt to varying operating conditions, minimising the actuation effort required for shape change remains a persistent challenge. Excessive actuation mass, structural complexity, and energy consumption may offset the aerodynamic performance benefits provided by morphing capability. One promising approach to tackle these problems is to use dynamic response to actuate the structures at resonance. For example, actuating bending dominated morphing structures with integrated piezoelectric materials near their resonance frequency can produce significant displacements with reduced energy requirements. However, in this case, the actuation frequency is limited to the resonance frequency, as determined by the mass, stiffness, and damping of the structure within its operating environment, which may constrain the application scenarios. If instead, a stiffness tuning mechanism is integrated into the system, then resonance across a broader range of actuation frequencies would be possible by actively tuning the system stiffness. In the current study, a mechanism for achieving tunable stiffness in the context of a bending dominated morphing structure is first proposed. The mechanism can increase or reduce the structure stiffness, which can eventually change the resonance frequency. A theoretical analysis and finite element simulation are then performed to investigate the structural properties of the mechanism. Based on the specific stiffness of a particular camber morphing concept, the stiffness tuning mechanism is then optimised to expand the range of obtainable stiffnesses. At last, an experimental demonstrator is built to validate the mechanism by measuring the trailing edge displacement when the resonance actuation is applied with varying actuation frequencies. The concept is validated on a morphing trailing edge mechanism, illustrating its practical potential in aerospace structures requiring frequency-adaptive actuation.
This study investigates the spatiotemporal evolution and dynamic microwave absorption properties of ferrofluid interfaces under lateral confinement and vertical magnetic excitation. We examine the morphological transition of a magnetite-based ferrofluid in a confined cylindrical domain (D = 2 cm) subjected to oscillating magnetic fields (Bmax = 40.5 mT, f = 0-105 Hz). Electromagnetic characterization in the Ku-band (12-18 GHz) was conducted using a metal-backed configuration to explicitly isolate Reflection Loss (RL). Results reveal that microwave absorption is governed by a synergy between bulk dissipation and surface geometry. While static layers exhibit thickness-dependent absorption, dynamic excitation enables an active "geometric enhancement" mechanism. We demonstrate an optimal operational window at low frequencies (f = 15-35 Hz) for thick layers (d >= 7 mm), where the stable ferromagnetic soliton functions as a gradient-index impedance matcher. Although the absolute peak RL of -9.46 dB at 17.8 GHz is numerically modest compared to traditional solid-state absorbers, its significance lies in its in-situ, reversible spatial tunability. This dynamic topology minimizes surface reflection and maximizes energy coupling, establishing a design strategy for adaptive electromagnetic absorbers capable of switching their reflection characteristics through precise fluid control.
