Abstract Light-responsive liquid crystal networks (LCNs) enable remotely controlled deformation and locomotion through programmed molecular alignment. However, reliable actuator operation requires a quantitative understanding of the relations between illumination, temperature, deformation, force generation, and locomotion responses, which are commonly investigated separately or under different experimental conditions. Here, we characterize these coupled responses in a 70 µm-thick monolithic splay-aligned LCN actuator containing an azobenzene chromophore and driven by localized 450 nm illumination designed for inching locomotion. Surface temperature and effective bending curvature are measured as functions of incident irradiance, and the corresponding blocking-force output is quantified. Short-term cyclic force response is evaluated using three films fabricated in separate batches, each subjected to 100 consecutive light-on/off cycles. Light-guided inching locomotion is then quantified on cardboard and PMMA substrates. Stable inching occurred at intermediate irradiance, and the actuator-length-normalized locomotion rates were 0.95 ± 0.08 and 1.18 ± 0.03 actuator lengths per minute on cardboard and PMMA, respectively. We thus establish quantitative relationships among optical input, heating, deformation, force generation, and locomotion in a monolithic light-responsive LCN actuator.
Abstract Accurate measurement of bolt preload is critical for ensuring the performance and operational safety of mechanical structural connections. However, traditional modal-conversion-based longitudinal and transverse wave testing methods suffer from low transverse wave excitation efficiency, insufficient signal stability, and limited feature-extraction accuracy. Taking an M36×3-350 mm bolt as the research subject, this study proposes an ultrasonic testing method based on direct excitation of transverse waves using the D15 shear mode of a piezoelectric transducer. It simultaneously incorporates the / time ratio feature to characterize the preload and employs a cubic spline interpolation algorithm to reconstruct the discretely sampled signals, thereby improving peak localization accuracy and enhancing the precision of time-of-flight extraction. The research results indicate: (1) Compared to traditional modal conversion excitation methods, direct excitation via the D15 shear mode significantly improves transverse wave excitation efficiency and modal purity. (2) The sound time ratio between transverse and longitudinal waves exhibits a significant linear negative correlation with bolt pretension and can serve as an effective feature parameter for characterizing changes in bolt pretension; numerical simulations agree well with experimental results, with the overall measurement error controlled within 3%. (3) The use of a cubic spline interpolation algorithm enables the raw sampling interval to be increased from 0.01 μs to 0.0001 μs, improving the positioning accuracy of the first-arrival time of ultrasonic waves and enabling the effective identification of minute changes in preload. These research findings provide a theoretical foundation and technical support for bolt connection condition monitoring and intelligent structural health monitoring under complex operating conditions.
Abstract Enabling multi-degree-of-freedom (multi-DOF) motion within limited space is a key challenge for expanding the application of piezoelectric ultrasonic actuators. Existing schemes achieve multi-DOF actuation through combinations of multiple stators or multimodal coupling, but they suffer from difficulties in miniaturization and complex multiple voltage input. This paper proposes a frequency-switching dual-DOF piezoelectric ultrasonic actuator based on designing multiple coupled resonance modes. By employing an irregular stator structure and dimensional optimization, two groups of coupled resonance modes are excited at different frequencies, producing elliptical motion trajectories in two DOFs. Dynamic simulations verify the principle of frequency-switching dual-DOF actuation. Experimental results demonstrate that the prototype achieves dual coupled modes at 113 kHz and 142 kHz for the X axis and Y axis, respectively. The maximum speed and resolution can be 320.62 mm/s and 0.47 μm. Moreover, the piezoelectric ultrasonic actuator can manipulate objects in the XOY plane with dual-DOF simply by frequency switching. This work provides a new design scheme with simple signal control and high integration for multi-modal multi DOFs ultrasonic actuation design.
Abstract Accurate tracking of piezoelectric fast steering mirrors (FSMs) is limited by rate-dependent hysteresis and cross-axis mechanical coupling. We developed a composite controller in which a Mamba-based joint dual-axis inverse model generates feedforward commands from coupled reference histories, while proportional–integral–derivative (PID) feedback corrects residual errors. The contribution lies in the system-level integration of joint inverse modeling, implicit inter-axis compensation, and residual feedback for the evaluated fixed-load fast steering mirror, based on a Mamba inverse model. Across four trajectories, the Mamba inverse model achieved lower mean spatial tracking error than the evaluated rate-dependent Prandtl–Ishlinskii (RDPI) and multi-nonlinear autoregressive moving average L2 (Multi-NARMA-L2) baselines. For the asynchronous Lissajous trajectory, its mean Euclidean root-mean-square tracking error was 8.63 μrad, compared with 10.65 μrad for Multi-NARMA-L2 and 36.49 μrad for RDPI. Computational tests characterized complementary execution modes. For length-1024 whole-sequence inference on an RTX 2060 GPU, Mamba required 0.63 ms, whereas long short-term memory (LSTM) required 4.21 ms. In a single-thread host-CPU recurrent-kernel benchmark, Mamba and LSTM required 45.84 μs and 30.74 μs per step, respectively. Together, these results show that Mamba-based inverse modeling improves feedforward tracking, while its integration with residual PID feedback supports closed-loop disturbance rejection for the evaluated fixed-load FSM.
Abstract This article presents an innovative design for shape changing ground robots, inspired by the miura origami fold (miura-ori). We focus on a triangular miura-ori shell (tri miura-ori) and its associated metamaterial. Currently, shape changing robots face significant challenges, including payload, complex control systems, and intricate manufacturing processes. Our origami-based design addresses these issues through a simplified, single degree of freedom mechanism that can be fabricated using two-dimensional (2D) prototyping techniques without the need for assembly. The robot, powered by a single encoder motor-cable system and manipulated via eight cables, shows remarkable flexibility and adaptability. Loading experiments have demonstrated that a single module of the robot can support 14.7 times its own weight. Notably, it achieves a high fold-to-deploy ratio of 55.6% (considering its thickness and mechanics), operational simplicity, and enhanced rigidity. Taking advantage of these characteristics, we prototyped this origami design as a shape changing robot. The ability to change shape enables the robot to adapt to cargo of varying shapes and sizes, carry heavy loads. It has potential for logistics transportation and ground rescue operations. This research contributes to the field of robotics by offering a streamlined and efficient approach to creating origami-inspired shape changing robots, leveraging the advantages of miura-ori folding patterns.
Abstract Kirigami metamaterials demonstrate significant potential in aerospace applications due to their unique cutting pattern designs and exceptional buckling deformation capabilities. Unlike conventional kirigami electromagnetic devices that mainly exploit geometric deployment for single-function tuning, this study investigates the electromagnetic properties of geometrically symmetric and asymmetric kirigami unit cells under tensile loading and uses their post-buckling morphologies as programmable resonator states for multifunctional electromagnetic reconfiguration. Controlled out-of-plane buckling configurations were achieved through stretching and subsequently converted into electromagnetic components for performance characterization. Analysis revealed distinct electromagnetic responses linked to buckling-induced structural transformations. Full-wave simulations predict that the combinatorial arrays can provide enhanced gain and that the cross-arranged arrays can further modify the electromagnetic response through inter-unit interactions. The simulated reconfigured kirigami metamaterials exhibit more than 10 dB monostatic RCS reduction at representative frequencies, while the experimentally measured S_{11} responses validate the mechanically induced frequency-reconfiguration behavior from the planar to the post-buckled states. This approach offers a component-free mechano-electromagnetic strategy for multiband reconfigurable metamaterials with reduced radar signatures.
Abstract Corrigendum: Soft inflatable kirigami actuators for wearable applications (2025 Smart Mater. Struct. 34 085019)
Abstract Detecting the direction of low-speed mechanical motions without a battery supply is critical for various industrial and environmental monitoring applications. This paper presents a battery-free bipolar detector that utilizes the intrinsic bipolar voltage output of a quasi-static toggling (QST) electromagnetic energy harvester. The QST harvester uniquely produces positive or negative voltage pulses in response to opposite motion directions, thereby directly encoding directional information into the harvested electrical signal without requiring additional sensing elements. A dedicated low-power circuit identifies the voltage polarity to determine the motion direction and simultaneously harvests energy to power a system-on-chip for wireless transmission. As a concrete demonstration, an application of a motion detector for cargo landing/lifting monitoring was proposed. The QST harvester releases a sufficient amount of energy above the internet of things (IoT) task’s requirements once it crosses a critical position. The mechanical-to-electrical energy conversion occurs even at a near-zero moving speed. This inherent property renders the QST energy harvester highly suitable for cargo loading and unloading scenarios. Meanwhile, the bipolar voltage-sensing mechanism integrated in the proposed system has demonstrated its technical feasibility and promising application potential. The proposed design provides a practical example of a simultaneous sensing and energy harvesting approach for developing sustainable ambient IoT systems.
Abstract Composite structures are vulnerable to impact-induced local anomalies that are often difficult to detect visually, necessitating structural health monitoring (SHM) methods capable of both impact localization and post-impact assessment. This study proposes an event-triggered passive–active SHM framework for a piezoelectric transducers (PZT)-embedded smart composite panel. The embedded PZT array functions as a unified sensing and actuation network, enabling a continuous procedure of passive impact localization and active damage detection. A PZT-embedded smart composite panel was fabricated, and tensile tests were performed to verify the mechanical compatibility of the sensor integration scheme. In the passive stage, impact localization was achieved through continuous wavelet transform, first-peak extraction, Gaussian-windowed cross-correlation, and probabilistic imaging. In the active stage, two time–frequency features, the energy ratio (ER) and the normalized residual entropy (NRE), were extracted from differential guided-wave responses, and a Mahalanobis-distance (MD)-based indicator was constructed within a healthy reference space for state discrimination. Experimental results demonstrate that the passive method achieves high-accuracy impact localization. Furthermore, while ER characterizes the energy of damage-induced scattering waves, NRE provides complementary information regarding the temporal energy redistribution of the residual signals. By synthesizing ER and NRE, the proposed MD-based indicator is shown to effectively separate healthy and local-anomaly states. Compared with conventional damage indices, the proposed method more effectively suppresses healthy-state fluctuations and improves discrimination stability.
Abstract Structural health and usage monitoring of aerospace structures requires scalable, lightweight, and minimally intrusive sensing solutions capable of supporting real-time digital twin frameworks for individual aircraft life management. This work presents the development and characterization of flexible printed strain gauges as a core sensing component of the IoT sensing skin developed within the H2020 AVATAR project, dedicated to developing a digital twin framework for real-time health and usage monitoring of transformative air vehicles. Silverbased and carbon-based strain gauges were manufactured using direct ink writing (DIW) and benchmarked against commercial metal foil gauges on carbon fibre reinforced polymer (CFRP) composite plates under tension, compression, bending, and fatigue loading. Silverbased gauges achieved a gauge factor of approximately 2.75 with good linearity, comparable to commercial gauges, making them well-suited for quantitative fatigue usage monitoring. Carbon-based gauges achieved gauge factors of 10-12 under tension with good repeatability over 2000 fatigue cycles, demonstrating high sensitivity for in-service monitoring. Substratefree solution, rosette multiaxial configurations, and embedding within CFRP laminates were successfully demonstrated. Temperature sensitivity and long-term drift are identified as the primary remaining challenges. The results establish DIW-printed strain gauges as a scalable and low-cost pathway toward the flexible IoT sensing skins required for digital twin-based aerospace Structural Health Monitoring (SHM), supporting the transition from fleet-level to individualized, condition-based structural life assessment.
Abstract Vibration-based structural damage detection under non-stationary excitation remains challenging due to the complex time-varying characteristics of structural responses and the limited availability of labeled data. Although existing unsupervised deep learning approaches have demonstrated potential for extracting damage-sensitive features, many of them rely on reconstruction errors as damage indicators, which may not sufficiently characterize the intrinsic evolution of structural states in the latent feature space. To address this limitation, an unsupervised structural damage detection framework based on wavelet transmissibility pattern spectra (WTPS), deep convolutional autoencoder (DCAE), and density-based clustering is proposed in this study. First, WTPS is employed to transform non-stationary vibration responses into damage-sensitive time–frequency representations. Subsequently, a spliced WTPS representation is constructed by combining the retained intact baseline WTPS with the WTPS obtained from newly acquired unlabeled monitoring responses. The DCAE is then utilized to extract compact latent features from WTPS image patches, while density-based spatial clustering of applications with noise clustering is introduced to identify damage-induced feature distribution variations and emerging cluster patterns. Different from conventional reconstruction-error-based unsupervised approaches, the proposed method identifies structural state changes through latent feature clustering rather than empirical reconstruction-error thresholds. Numerical simulations on a simply supported beam and experimental validation on a full-scale steel tower benchmark model demonstrate that the proposed framework can effectively detect the investigated single and multiple damage scenarios under non-stationary excitation. In particular, the framework successfully identifies stiffness reductions ranging from slight damage (10%) to severe damage (30%) in numerical simulations and structural changes induced by bolt loosening in experimental tests. The results demonstrate the potential of the proposed method for unsupervised structural health monitoring under complex operational conditions.
Abstract Stiffness-tunable mechanical metamaterials have attracted significant attention due to their potential for adaptive structures and systems. However, current designs often suffer from limited tuning ranges or discontinuous variations in stiffness. To address these challenges, this study presents a novel rotation-based mechanical metamaterial unit capable of continuous stiffness modulation via angular adjustment. Experimental and numerical analyses demonstrate an exceptional 22-fold variation in stiffness, ranging from 123.71 N mm −1 to 2751.52 N mm −1 , significantly outperforming existing tunable metamaterials. Parametric studies reveal the influence of key geometric parameters, including spoke width, cutout radius, and thickness, on stiffness behavior, while structural arrangement strategies further enhance the mechanical programmability. This work establishes a robust foundation for the design of adaptive systems with broad applicability in fields such as robotics, aerospace, and precision machinery.
Abstract This Roadmap covers the status, current challenges, and new science required to address these challenges, for mechanical metamaterials. This is timely, given the sustained increase in mechanical metamaterials research outputs and patents. Sections are grouped according to anomalous properties. These include auxetic (negative Poisson’s ratio), sub-divided into geometric form, flexible and shape morphing, negative thermal expansion, and negative stiffness. Specific sections also cover naturally and biologically inspired metamaterials (characterised by complex multiscale material architectures), and nanoscale/molecular metamaterials, and applications of mechanical metamaterials. Key challenges raised throughout relate to scaling up manufacturing for reliable, cost-effective production, particularly for high resolution processes nearing the molecular level, and efficiency of modelling approaches required for these multiscale systems. With opportunities to target their new, useful functionalities, such as the extreme hardness of auxetic materials, or extreme vibration damping via negative stiffness, mechanical metamaterials are contributing to rapid evolution of manufacturing and multiscale modelling.
Abstract Multifunctional conductive polymer composites (CPCs) capable of electrically activated actuation have attracted increasing attention for smart materials and 4D printing applications. In this work, conductive poly(ϵ-caprolactone)/ethylene–glycidyl methacrylate/carbon black (PCL/E-GMA/CB) composites were prepared. The immiscible PCL/E-GMA blend forms a co-continuous morphology, while CB particles preferentially localize within the E-GMA phase, leading to a morphology-driven double percolation structure that promotes conductive network formation at relatively low filler loadings. Rheological analysis confirmed the formation of a percolated filler network at approximately 2 phr of CB. The electrical percolation threshold was observed at approximately 10 phr and 8 phr of CB for superficial and volumetric conductivity, respectively, while the electrical conductivity reached 3 × 10 −5 S cm −1 at 12 phr of CB. The resulting conductive pathways enable efficient Joule heating, allowing electrically triggered shape recovery without external heating sources. The composites containing 12 phr of CB exhibited shape fixity ratios above 95% and shape recovery ratios above 82%, together with triple-shape memory behavior and self-healing capability. The materials were successfully processed into filaments and fabricated through 4D printing, demonstrating their potential for producing electrically responsive structures with complex geometries. Overall, the results demonstrate that the synergistic combination of reactive compatibilization, selective filler localization, and morphology-driven double percolation in immiscible polymer blends provides an effective strategy for designing advanced multifunctional CPCs for applications such as soft robotics, actuators, and adaptive structures.
Abstract The transient response of the controllable negative stiffness (NS) structures filled with magnetorheological fluids (MRFs) to square-wave magnetic field was investigated experimentally. The dynamic response characteristics of the magnetic field source to step up and step down excitations were measured. The samples of NS structure filled with MRFs were prepared and compressed with a constant speed. The square-wave magnetic field responses of the structures were obtained by controlling the switching time of DC power supply under constant current conditions, and the force displacement envelope curves were formed by experiments with different square-wave starting times. Experimental results indicate that the response of the NS structures to step rising magnetic field can be described by the bi-exponential model with a fast and a slow characteristic time. The fast response characteristic times are consistent with the response times of the magnetic field source to step up excitation, and the variation of pressing force is the dynamic response of MRFs in the structure to step rising magnetic field. The slow response times are tens of times longer than the corresponding fast response times, the reason is that the particles inside MRFs can be structured at the presence of magnetic field with the small shear rate of MRFs during compression slowly which induces the pressure continues to increase. The response times of the NS structures to step falling magnetic field are also essentially identical to the response times of the magnetic field source to step down excitation. The results provide important insights into the dynamic response of controllable NS structures, which can promote the applications in vibration control.
Abstract Variable-thickness structures are extensively employed in aerospace applications due to their superior weight-to-strength ratios and structural efficiency. However, thickness variations introduce complex guided wave propagation characteristics, including dispersion distortion, path deviation, and energy redistribution, that challenge conventional damage imaging methods based on uniform-thickness assumptions. In this study, the impact of thickness variation on the signal difference coefficient (SDC) is first investigated via numerical simulations, revealing that SDC sensitivity for equivalent damage is significantly higher in thin sections than in thick ones. To address this imbalance, an enhanced probabilistic damage imaging method is proposed by incorporating a thickness weighting factor. This factor which derived from the spatial configuration of sensor paths and local thickness gradients, adapts the elliptical probability distribution to compensate for thickness-induced SDC bias. Experimental validation on a variable-thickness aluminum plate with dual defects demonstrates that the proposed method overcomes the limitations of the conventional RAPID algorithm, such as weak detection in thick regions and imaging artifacts. The improved algorithm achieves precise, simultaneous localization of multiple damage sites with superior energy concentration and minimal mutual interference. The proposed approach effectively enhances the resolution and reliability of structural health monitoring (SHM) for complex variable-thickness aerospace components.
Abstract The properties of porous fibre networks, made from metal fibres, are typically dependent on their network architecture, which encompasses two- or three-dimensional structural features associated with the arrangement of metal fibres. Consideration of NiTi fibres in a porous fibre network also influences their characteristic shape memory effect and superelasticity properties. In order to quantify this dependence, the present study considers a porous single-layer NiTi shape memory alloy fibre network. Use of analytical approach in it has identified that porosity, fibre inclination angle, number of vertical fibres and vertical segment length are the independent parameters that affect its deformation behaviour. The effect of these parameters was quantified by modelling multiple porous single-layer NiTi fibre networks using Solidworks® and ANSYS®. Vertical fibres of 25 mm length and fibres with 15°, 30° and 45° inclination angle were considered to create multiple porous single-layer networks with porosity ranging from 82% to 92%. Also, vertical segment length was made to vary between 1 mm and 3 mm. These networks were subsequently subjected to superelastic loading and it was observed that increase in porosity increased the inclined segment length causing overall decrease of superelastic hysteresis. In addition, the maximum plateau load withstood by the porous single-layer NiTi, before unloading shows a decreasing trend (19.02 N to 8.86 N for 45° inclined fibre) with increase in porosity. Further, fibres with lower inclination angles (15°) were seen to have longer inclined segment lengths (5.14 mm at 82% porosity) and more efficient in sustaining higher plateau loads (19.53 N). The force experienced by 15° and 45° fibres was highest when the vertical segment length was 3 mm as compared to that of 1 mm. Thus, tailoring the architecture of porous single-layer NiTi network can lead to targeted development of three-dimensional metallic fibre networks applicable to biomedical- and automotive-industries.
Abstract Thin-walled plate-like structures are susceptible to local stiffness and damping variations induced by long-term loading, impact, and environmental disturbances, making accurate damage localization essential for structural health monitoring. Conventional electromechanical impedance (EMI) methods often rely on handcrafted damage indices, whereas single-stream learning models may not fully exploit the complementary information contained in the real and imaginary impedance components. This study proposes a difference-aware multi-view Siamese Transformer (DA-MVST) framework for direct EMI-based damage-coordinate regression. The real and imaginary EMI spectra are represented as physically correlated Gramian angular summation field (GASF) views and mapped into a shared feature space using a shared-weight Siamese Transformer encoder. The resulting view-specific representations and their absolute difference are fused for two-dimensional coordinate prediction through a regression head configured using a genetic algorithm (GA). Sensor-wise token encoding and global average pooling (GAP) provide fixed-length representations across different numbers of piezoelectric transducer (PZT) channels, while ensemble averaging improves prediction stability. The framework was evaluated on an Al-7075 plate instrumented with a 16-element PZT array, covering 81 perturbation locations and three perturbation levels. DA-MVST achieved a mean Euclidean localization error of 3.52 ± 0.13 mm and outperformed the selected baseline regression pipelines. Ablation analysis further quantified the contributions of dual-view representation, shared-weight feature extraction, difference-aware fusion, GA-assisted configuration, and ensemble inference. Under controlled experimental conditions, the results demonstrate the effectiveness of DA-MVST for direct damage-coordinate regression from multi-sensor EMI responses.
Abstract Passive elastic metamaterials offer effective vibration attenuation through locally resonant bandgaps, but suffer from fundamental limitations in real-time tunability, narrow operational bandwidth, and non-adaptive behavior. This work presents an experimental framework for inducing and tuning vibration bandgaps in digitally controlled mechatronic metamaterials. A slender-beam structure instrumented with collocated piezoelectric sensor–actuator pairs distributed periodically along the length is used as the host medium, with decentralized second-order low-pass resonant filter with negative position feedback controllers implemented in real time on an FPGA platform. Unlike conventional approaches that assess bandgap formation through lateral tip displacement, this study motivates the choice of bandgap indicator through bending strain minimization at the piezoelectric sensors, and validates bandgap formation quantitatively through end-to-end closed-loop transmissibility. This more accurately reflects the moment-based phase cancellation dynamics underlying resonant actuation. Closed-form analytical expressions for transmissibility in a general n × n decentralized feedback architecture are derived via block elimination and experimentally validated using the 7 × 7 unit-cell configuration. The results demonstrate that targeted low-frequency bandgaps in the range of 20–100 Hz can be systematically induced and reshaped through programmable tuning of controller gain and damping ratio, significantly improving vibration attenuation. By shifting the focus to localized dynamics, this work deepens the understanding of how control-induced bandgaps emerge and demonstrates a scalable pathway for designing programmable mechatronic metamaterials based on digitally synthesized resonator dynamics.
