Search Results (247)

The identification of structural damage types remains a key challenge in electromechanical impedance/admittance (EMI/EMA)-based structural health monitoring realm. This paper proposed a damage classification approach for concrete structures by using integrating discrete wavelet transform (DWT) decomposition of EMA signatures with supervised machine learning. In this approach, the EMA signals of arranged piezoelectric ceramic (PZT) patches were successively measured at initial undamaged and post-damaged states, and the signals were decomposed and processed using the DWT technique to derive indicators including the wavelet energy, the variance, the mean, and the entropy. Then these indicators, incorporated with traditional ones including root mean square deviation (RMSD), baseline-changeable RMSD named RMSDk, correlation coefficient (CC), and mean absolute percentage deviation (MAPD), were processed by a support vector machine (SVM) model, and finally damage type could be automatically classified and identified. To validate the approach, experiments on a full-scale reinforced concrete (RC) slab and application to a practical tunnel segment RC slab structure instrumented with multiple PZT patches were conducted to classify severe transverse cracking and minor crack/impact damages. Experimental and application results cogently demonstrated that the proposed DWT-based approach can precisely classify different types of damage on concrete structures with higher accuracy than traditional ones, highlighting the potential of the DWT-decomposed EMA signatures for damage characterization in concrete infrastructure. Full article

Eczema, or atopic dermatitis (AD), is a chronic inflammatory skin condition characterized by persistent itching and scratching, significantly impacting patients’ quality of life. Effective monitoring of scratching behaviour is crucial for assessing disease severity, treatment efficacy, and understanding the relationship between itch and sleep disturbances. This review explores current technological approaches for detecting and monitoring scratching and itching in AD patients, categorising them into contact-based and non-contact-based methods. Contact-based methods primarily involve wearable sensors, such as accelerometers, electromyography (EMG), and piezoelectric sensors, which track limb movements and muscle activity associated with scratching. Non-contact methods include video-based motion tracking, thermal imaging, and acoustic analysis, commonly employed in sleep clinics and controlled environments to assess nocturnal scratching. Furthermore, emerging artificial intelligence (AI)-driven approaches leveraging machine learning for automated scratch detection are discussed. The advantages, limitations, and validation challenges of these technologies, including accuracy, user comfort, data privacy, and real-world applicability, are critically analysed. Finally, we outline future research directions, emphasizing the integration of multimodal monitoring, real-time data analysis, and patient-centric wearable solutions to improve disease management. This review serves as a comprehensive resource for clinicians, researchers, and technology developers seeking to advance objective itch and scratch monitoring in AD patients. Full article

Wireless structural health monitoring is needed for machine elements of which the working motions prevent wired monitoring. Rotating machine shafts are such elements. Wired monitoring of the rotating shaft requires making significant changes to the shaft structure, primarily drilling a hole in the longitudinal axis of the shaft and installing a slip ring assembly at the end of the shaft. Such changes to the shaft structure are not always possible. This paper proposes the use of piezoelectric energy harvesting from a rotating shaft to power wireless temperature monitoring of the shaft surface. The main components of presented wireless temperature monitoring are three piezoelectric composite patches, three thermal fuses, a system for storing and distributing the harvested energy, and a radio transmitter. This article contains the results of experimental research of such wireless monitoring on a dedicated laboratory stand. This research included four connections of piezoelectric composite patches: delta, star, parallel, and series for different capacities of a storage capacitor. Based on experimental results, three parameters that influence the frequency of sending data packets by the presented wireless temperature monitoring are identified: amplitude of stress in the rotating shaft, rotation speed of the shaft, and the capacity of a storage capacitor. Full article

Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. This study presents an integrated hybrid SHM-NDT methodology enhanced by deep learning to enable the real-time monitoring and classification of mechanical stresses in structural components. As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. The hybrid system utilized an ultrasonic transducer (NDT) for excitation and piezoelectric sensors (SHM) for signal acquisition. Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. The AI-enhanced approach achieved 100% accuracy in 47 out of 48 tests and 94% in the remaining tests. These results demonstrate that the hybrid SHM-NDT framework, combined with machine learning, offers a powerful and adaptable solution for continuous monitoring and precise damage assessment of structural systems, significantly advancing maintenance practices and safety assurance. Full article

This paper proposes a method for determining the contact temperature in the secondary shear zone. The input data include the results of the experimental tests of the orthogonal turning of a Ti-6Al-4V titanium workpiece using uncoated WC-Co tools with a flat rake face. The cutting force components were recorded using a piezoelectric dynamometer, a thermovision camera was used to record the temperature in the cutting zone, and a high-speed camera was used to record the chip-forming process. The independent variables included machining parameters, feed rate, cutting speed, and rake angle. A dual-zone thermomechanical cutting process model that accounted for the sticking and sliding areas was adapted for the identification of the heat flux in the chip–rake face contact zone. Then, based on the Shaw approach, the partition coefficients were determined for the contact temperature on the chip–tool tip contact. In addition, the results of the experimental tests allowed the determination of the relationship among the process parameters, friction coefficients, and the length of the contact of the chip with the tool rake face. A graphical visualization of the temperature distribution on the tool rake face was performed using the MATLAB PDE 3.9 software package. Although the application of the dual-zone model has been well presented in the literature, the results provided in this paper may be helpful in analyzing and modeling thermal phenomena in the secondary shear zone. Full article

Materials with perovskite phases are widely used in solar cells and ferroelectric, piezoelectric, dielectric and superconducting devices due to their various notable functions. However, structural instability limits some compositions in forming robust perovskite phases for device applications. The analytical approach using the tolerance factor (t) can only guarantee prediction accuracy within a limited range, ascribed to its nature of overlooking the atomic interaction. Hence, here we establish a prediction model using formation energy as the target parameter for its reflection of the reaction of atoms and apply machine learning as the analysis method since it has been successfully employed in plenty of material property prediction studies. Machine learning employs statistical methodologies to identify correlative patterns within large-scale datasets, enabling accurate predictions with robust generalization. In this work, we built a model to predict the formation energy of ABX₃ perovskite using machine learning and achieved a model with an R-squared value of 0.928 and a root mean square error of 0.301 eV/atom, validated by first-principles computations. In total, 75% of the values were correctly predicted within an error lower than 0.06. This work could contribute to accelerating the study of solving perovskites’ instability. Full article

Gas bubbles are one of the main disturbances encountered when dispensing drugs of microliter volumes using portable miniaturized systems based on piezoelectric diaphragm micropumps. The presence of a gas bubble in the pump chamber leads to the inaccurate administration of the required dose due to its impact on the flowrate. This is particularly important for highly concentrated drugs such as insulin. Different types of sensors are used to detect gas bubbles: inline on the fluidic channels or inside the pump chamber itself. These solutions increase the complexity, size, and cost of the microdosing system. To address these problems, a radically new approach is taken by utilizing the sensing capability of the piezoelectric diaphragm during micropump actuation. This work demonstrates the workflow to build a self-sensing micropump based on artificial intelligence methods on an embedded system. This is completed by the implementation of an electronic circuit that amplifies and samples the loading current of the piezoelectric ceramic with a microcontroller STM32G491RE. Training datasets of 11 micropumps are generated at an automated testbench for gas bubble injections. The training and hyper-parameter optimization of artificial intelligence algorithms from the TensorFlow and scikit-learn libraries are conducted using a grid search approach. The classification accuracy is determined by a cross-training routine, and model deployment on STM32G491RE is conducted utilizing the STM32Cube.AI framework. The finally deployed model on the embedded system has a memory footprint of 15.23 kB, a runtime of 182 µs, and detects gas bubbles with an accuracy of 99.41%. Full article

Structural symmetry significantly influences the fundamental characteristics of two-dimensional (2D) materials. In conventional transition metal dichalcogenides (TMDs), the absence of in-plane symmetry introduces distinct optoelectronic behaviors. To further enrich the functionality of such materials, recent efforts have focused on disrupting out-of-plane symmetry—often through the application of external electric fields—which leads to the generation of an intrinsic electric field within the lattice. This internal field alters the electronic band configuration, broadening the material’s applicability in fields like optoelectronics and spintronics. Among various engineered 2D systems, Janus transition metal dichalcogenides (JTMDs) have shown as a compelling class. Their intrinsic structural asymmetry, resulting from the replacement of chalcogen atoms on one side, naturally breaks out-of-plane symmetry and surpasses certain limitations of traditional TMDs. This unique arrangement imparts exceptional physical properties, such as vertical piezoelectric responses, pronounced Rashba spin splitting, and notable changes in Raman modes. These distinctive traits position JTMDs as promising candidates for use in sensors, spintronic devices, valleytronic applications, advanced optoelectronics, and catalytic processes. In this Review, we discuss the synthesis methods, structural features, properties, and potential applications of 2D JTMDs. We also highlight key challenges and propose future research directions. Compared with previous reviews, this work focusing on the latest scientific research breakthroughs and discoveries in recent years, not only provides an in-depth discussion of the out-of-plane asymmetry in JTMDs but also emphasizes recent advances in their synthesis techniques and the prospects for scalable industrial production. In addition, it highlights the rapid development of JTMD-based applications in recent years and explores their potential integration with machine learning and artificial intelligence for the development of next-generation intelligent devices. Full article

Piezoelectric polymer composites, particularly polyvinylidene fluoride (PVDF) blended with barium titanate (BT), show promise for wearable technologies as both energy harvesters and haptic actuators. However, these composites typically exhibit limited electromechanical coupling and insufficient β-phase formation. This study presents a novel approach using ionic liquids (ILs) to enhance PVDF-based piezoelectric composite performance. Through solution-casting methods, we examined the effect of IL concentration on the structural, mechanical, and piezoelectric properties of PVDF/BT composites. Results demonstrate that the use of IL significantly improves β-phase crystallization in PVDF while enhancing electrical properties and mechanical flexibility, which are key requirements for effective energy harvesting and haptic feedback applications. The optimized composites show a 25% increase in β-phase content, enhanced flexibility, and a 100% improvement in piezoelectric voltage output compared to other more conventional PVDF/BT systems. The IL-modified composite exhibits superior piezoelectric response, generating an output voltage of 9 V and an output power of 40.1 µW under mechanical excitation and a displacement of 138 nm when subjected to 13 V peak-to-peak voltage, making it particularly suitable for haptic interfaces. These findings establish a pathway toward high-performance, flexible piezoelectric materials for multifunctional wearable applications in human–machine interfaces. Full article

In contrast to traditional hydrogels, which are susceptible to water evaporation and structural degradation, non-hydrogel materials are engineered for superior stability and consistent performance. Here, we report an innovative piezoelectric polyvinyl chloride/multi-walled carbon nanotube polymer gel (PVC/MWCNT polymer gel, PMPG) with exceptional linearity (as low as 1.31%), high sensitivity (50–310.17 mV), rapid response (172–189 ms), and thermal stability. Under strain induction, ordered rearrangement of dipoles in PMPG and the enhancement of MWCNTs generate a potential difference. Increasing MWCNT content enhances output voltage, sensitivity, conductivity, maximum stress, Young’s modulus, and toughness, while reducing nonlinear error. Higher dibutyl adipate (DBA) content increases output voltage and slightly improves sensitivity but decreases mechanical strength. The optimal PMPG (PVC:DBA = 1:5, 1 wt% MWCNTs) exhibited outstanding performance. It exhibits a nonlinear error as low as 1.31%, a conductivity of 25.4 μS/cm, an 80% compressive strain tolerance (273 kPa stress), and dimensional stability for 90 days in air. By integrating PMPG with machine learning algorithms, soft robotic grippers gain advanced contact perception capabilities, enabling applications in medicine, rescue, exploration, and other fields requiring fine manipulation and adaptability. This work highlights PMPG’s potential as a stable, high-performance material for soft robotics and beyond. Full article

Human–machine interfaces based on force touch panels have attracted enormous attention due to the merits of the high human–machine interaction efficiency. Many studies have been devoted to diverse force touch technologies. Broad applications in terms of both actual use and research have been developed, such as 3D touch and force-based keystroke authentication. The fruitful results are based on the assumption that users’ touch habits remain unchanged over time; thus, a stationary customized force-sensing model can be built. However, for long-term use, users’ touch habits change due to time-drifting and specific events, causing a decrease in the performance of stationary force-sensing models. To address this issue, a rectified artificial neural network for long-term force sensing in piezoelectric touch panels is presented in this paper. With additional information on the touching time and the occurrence of specific events, the force level predictions were rectified, achieving an accuracy of 97.62% for a long-term data set. The proposed technique enables customized force sensing for long-term use and enhances the human–machine interactive efficiency. Full article

Human–machine interface (HMI) systems are increasingly utilized to develop assistive technologies for individuals with disabilities and older adults. This study proposes two HMI systems using piezoelectric sensors to detect facial muscle activations from eye and tongue movements, and accelerometers to monitor head movements. This system enables hands-free wheelchair control for those with physical disabilities and speech impairments. A prototype wearable sensing device was also designed and implemented. Four commands can be generated using each sensor to steer the wheelchair. We conducted tests in offline and real-time scenarios to assess efficiency and usability among older volunteers. The head–machine interface achieved greater efficiency than the face–machine interface. The simulated wheelchair control tests showed that the head–machine interface typically required twice the time of joystick control, whereas the face–machine interface took approximately four times longer. Participants noted that the head-mounted wearable device was flexible and comfortable. Both modalities can be used for wheelchair control, especially the head–machine interface for patients retaining head movement. In severe cases, the face–machine interface can be used. Moreover, hybrid control can be employed to satisfy specific requirements. Compared to current commercial devices, the proposed HMIs provide lower costs, easier fabrication, and greater adaptability for real-world applications. We will further verify and improve the proposed devices for controlling a powered wheelchair, ensuring practical usability for people with paralysis and speech impairments. Full article

This paper introduces a wearable silent text input system designed to capture text input through silent speech, without generating audible sound. The system integrates Electromyography (EMG) and piezoelectric lead zirconate titanate (PZT) sensors in a miniaturized form that can be comfortably attached to the chin, making it both comfortable to wear and esthetically pleasing. The EMG sensor records muscle activity linked to specific tongue and jaw movements, while the PZT sensor measures the minute vibrations and pressure changes in the chin skin caused by silent speech. Data from both sensors are analyzed to capture the timing and intensity of the silent speech signals, allowing the extraction of key features in both time and frequency domain. Several machine learning (ML) models, including both feature-based and non-feature-based approaches commonly used for classification tasks, are employed and compared to detect and classify subtle variations in sensor signals associated with individual alphabet letters. To evaluate and compare the ML models, EMG and PZT signals for the eight most frequently used English letters are collected across one hundred trials each. Results showed that non-feature-based models, particularly the Fea-Shot Learning with fused EMG and PZT signals, achieved the highest accuracy (95.63%) and F1-score (95.62%). The proposed system’s accuracy and real-time performance make it promising for silent text input and assistive communication applications. Full article

This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, midsection, and tip. The experimental procedure is easy, and only one low-cost piezoelectric accelerometer is needed, which is affordable and straightforward to install. The machine learning technique used requires a small dataset and low computing power. The results show exceptional performance, achieving an accuracy of 99.37% and a precision of 98.77%. This approach enhances the reliability of wind turbine blade monitoring. It provides a robust early detection and maintenance solution, improving operational efficiency and safety in wind energy production. K-nearest neighbors and decision trees are also used for comparison purposes. Full article

Piezoelectric-actuated machine tools (PAMTs) exhibit nanoscale motion capabilities, with their S-shaped curve trajectory further enabling smooth path execution and reduced terminal pulse. However, the speed changes inherent in multi-order trajectories introduce an additional non-uniform creep difference (NCD), which differs significantly from conventional hysteresis effects. Traditional models are inadequate for addressing this mixed shape nonlinearity. To overcome this limitation, this paper proposes a hybrid compensation method for the S-shaped curve trajectory of piezoelectric-actuated machine tools. The general deformation law is first established through a comprehensive mechanism analysis. The NCD and hysteresis, induced by speed changes and inherent properties, are decoupled and addressed using a pre-known phenomenon model and a clockwise operator model, respectively. Finally, a hybrid feedforward control strategy is developed to integrate these models for effective compensation. Experimental results demonstrate that the hybrid compensation method achieves a maximum relative error of 5.48% and a maximum mean square error of 0.28%, effectively mitigating the dual nonlinear factors arising from the piezoelectric-actuated machine tool’s trajectory in feedforward control. Full article