Piezoelectric Wafer Research Articles

Research on rotational rollback suppression of diamond-shaped active locking flexure mechanism (ALFM) driven by piezoelectric wafers

Research on rotational rollback suppression of diamond-shaped active locking flexure mechanism (ALFM) driven by piezoelectric wafers

Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

A radar transducer for unidirectionally emitting and steering SH guided wave

It is of practical importance to emit a pure wave mode, focus its energy along a given direction, and then steer the wave beam in guide-wave-based structural health monitoring (SHM) because it can quickly scan the overall structure. Such a goal is usually realized using a two-dimensional (2D) phased array, which requires many transducer elements and expensive electronics. This work proposed a radar transducer (RD-T) for unidirectionally emitting and steering the fundamental shear horizontal wave (SH0 wave). The proposed RD-T consists of an annular metasubstrate and several rectangular thickness-shear (d15) piezoelectric wafers. The metasubstrate is designed to provide the required phase gradient for unidirectionally emitting and sensing a pure SH0 wave, so no extra time delay is required for driving the RD-T. The beam steering is obtained by activating the subunits one by one. The SH0 wavefields generated by the subunit are described by a theoretical model and the effects of dimension parameters are analyzed. Finite element simulations and experiments are conducted to examine the performances of the RD-T. Both simulated and experimental results indicate that from 200 kHz to 270 kHz, the RD-T can unidirectionally emit an SH0 wave with a high SNR (signal-to-noise ratio) and steer the wave beam along different directions. The performance of the RD-T on damage detection is then investigated by pulse-echo experiments. It can be found that the RD-T can successfully distinguish symmetric defects and locate defects with an acceptable error. Compared with the traditional 2D phased array, the RD-T can realize 360° scanning of the overall structure more efficiently, exhibiting great potential in the field of SHM.

Effect of moisture on the sensing capabilities of piezoelectric actuator/sensor pairs on flax fibre reinforced composite laminates

The sensitivity of the piezoelectric actuators’ response to the moisture uptake has been investigated on flax fibre reinforced composite laminates. Non-woven flax fabric was impregnated with epoxy and composite laminates were fabricated using hand lay-up and cured in a compression moulding machine. The laminates were initially impacted with a drop-weight impact machine at a low energy level, with the aim to generate an artificial surface crack that will allow the moisture to enter the fibre-matrix interface more effectively. A set of piezoelectric actuators and sensors were adhered on the surfaces of the composite panels in a specific rectangular arrangement to allow for wave capturing at various directions. The wave characteristics of the samples were extracted in pristine stage and then the samples were impacted and immersed in distilled water until saturation. At regular intervals after the water immersion, the panels’ weight was measured using a laboratory scale and the wave propagation characteristics of the materials ware measured in order to investigate the gradual effect of moisture on the capabilities of the actuators and sensors. The propagation of stress waves in the dry and wet samples was investigated and the effect of moisture absorption was examined experimentally using piezoelectric wafers until the saturation was reached. Finally, a continuum damage mechanics layerwise time domain spectral finite element model, both for the composite laminate and the rich resin layers, was also developed to simulate the materials’ performance under impact and evaluate the wave propagation within the material at the dry state.

Application of deep learning for structural health monitoring of a composite overwrapped pressure vessel undergoing cyclic loading

Structural Health Monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite Overwrapped Pressure Vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the Digital Quality Infrastructure Initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure.

Monitoring of the preload in bolted connections with piezoelectric wafer active sensors

Piezoelectric wafer active sensors are very sensitive to changes of loads in structures, which makes them useful for the load monitoring. Unfortunately, these transducers are also sensitive to environmental effects such as a change of the ambient temperature. These effects can mask the information about the load in the recorded measurement data of the piezoelectric wafer active sensors. Therefore, the effect of the load needs to be distinguished from these masking effects of the environmental influences in order to avoid misleading information about the occurring loads of the monitoring system. In this work, two monitoring procedures, which are applicable with piezoelectric wafer active sensors, shall be applied in order to have two different sources of information and be able to distinguish between the load information and the temperature influence. One approach is based on electro-mechanical impedance measurements and the other one is based on acousto-ultrasonics. The strategy of combining these two information shall be used to monitor the preload in bolted joints. In an experimental setup, piezoelectric wafer active sensors have been attached to the bolt’s head and the face side of its shaft. Then, different preload stages were set. At every load stage, acousto-ultrasonics measurements and also electro-mechanical impedance measurements were carried out. First experiments show very promising results.

Monitorization of crack propagation in welded aluminium assemblies

Utilizing welded assemblies of aluminum profiles presents an effective means of reducing mass in rolling stock. However, these lightweight and optimally stiffened structures might be susceptible to unexpected failure modes that were not accounted for during the design phase. This could be due to insufficient information about in-service conditions or uncertainties in the material and manufacturing processes. This study aims to assess the feasibility of monitoring welded aluminum assemblies in real-time under both static and cyclic loading conditions to evaluate their structural health. Mechanical tests were conducted on welded aluminum profile assemblies while employing piezoelectric wafer active sensors (PWAS) for continuous monitoring. The PWAS facilitated ultrasonic active and passive inspections during testing. Passive sensing involved utilizing the acoustic emission technique to locate the source of acoustic signals associated with damaging mechanisms like crack nucleation and propagation. Upon identifying a detrimental event, on-demand active inspections using Ultrasonic Guided Waves were deployed to examine the structure and gather crucial information about its current condition based on analyzing temporal signal features. Although our mechanical tests were conducted in a laboratory environment, we recorded some temperature variations. Hence, we compensated for environmental influences through a data fusion model. Additionally, repetitivity analyses were conducted to confirm the reliability of the proposed inspection technique. Encouragingly, results demonstrated consistency and reliability in this proposed method.

Guided Wave Behavior in Type IV Composite Overwrapped Pressure Vessels Under Fatigue Loading

Innovative Ultrasonic Guided Wave Approach for Structural Health Monitoring of Type IV Composite Overwrapped Pressure Vessels Seyedreza Hashemi, Jan Heimann, Amir Charmi, Eric Duffner, Jens Prager Abstract: The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs). The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV. Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings. Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility . The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance.

Damage identification of honeycomb sandwich structures based on Lamb waves and 1D-CNN

A specific method combining Lamb waves and 1D-CNN is proposed in this paper for damage assessment of honeycomb sandwich structures, which can identify the location and degree of damage simultaneously. The applicability of the Lamb waves method is verified by simulating the interaction between the Lamb waves and different degrees of damage on the honeycomb panel. In the experiment, four identical piezoelectric wafers were placed on a honeycomb plate in a square shape, and a mass block was placed inside to simulate damage. One of the sensors was used as an actuator to generate the Lamb waves signal, and the remaining three sensors received the response signals after interacting with the damage. The collected signals were denoised by Kalman filter, and the time domain signals were transformed into the frequency domain signals by Fourier transform, so as to extract the feature more effectively. The amplitude frequency characteristics of Lamb wave signals were used as inputs to the 1D-CNN model, and the damage categories were used as outputs. 365 samples were used for model testing, 364 were correctly identified with a correct recognition rate of 99.7 %. The result shows that the proposed method is feasible for damage identification of honeycomb sandwich structures.

Piezotronic Transistors Based on GaN Wafer for Highly Sensitive Pressure Sensing with High Linearity and High Stability.

Piezotronic effect utilizing strain-induced piezoelectric polarization to achieve interfacial engineering in semiconductor nanodevices exhibits great advantages in applications such as human-machine interfacing, micro/nanoelectromechanical systems, and next-generation sensors and transducers. However, it is a big challenge but highly desired to develop a highly sensitive piezotronic device based on piezoelectric semiconductor wafers and thus to push piezotronics toward wafer-scale applications. Here, we develop a bicrystal barrier-based piezotronic transistor for highly sensitive pressure sensing by p-GaN single-crystal wafers. Its pressure sensitivity can be as high as 19.83 meV/MPa, which is more than 15 times higher than previous bulk-material-based piezotronic transistors and reaches the level of nanomaterial-based piezotronic transistors. Moreover, it can respond to a very small strain of 3.3 × 10-6 to 1.1 × 10-5 with high gauge factors of 1.45 × 105 to 1.38 × 106, which is a very high value among various strain sensors. Additionally, it also exhibits high stability (current stability of 97.32 ± 2.05% and barrier height change stability of 95.85 ± 3.43%) and high linearity (R2 ∼ 0.997 ± 0.002) in pressure sensing. This work proves the possibility of designing a bicrystal barrier as the interface to obtain a strong piezotronic effect and highly sensitive piezotronic devices based on wafers, which contributes to their applications.

Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks.

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth.

Optimization and operation of interdigital transducer to improve signal-to-noise ratio

This study presents a novel optimization method for Lamb wave mode selective transducers, surpassing traditional interdigital transducers (TIDT) by employing non-uniform electrode widths to suppress harmonic components. Utilizing a frequency domain finite element model (FDFEM), the coupling mechanism between the double-sided interdigital transducer (DS-IDT) and the waveguide is analyzed, forming the optimization basis. By discussing key parameters affecting wavelength selection characteristics, an optimization scheme is formulated. Employing a multi-objective genetic algorithm, the strategy focuses on minimizing the ratio of stray to target mode as the cost function, resulting in significant signal-to-noise ratio (SNR) improvements. Compared to TIDTs, SNR gains of at least 4.7 dB at 70 kHz and 8.8 dB at 400 kHz are achieved. Laboratory experiments validate the superior mode selection performance of the optimized interdigital transducer (OIDT) over ordinary piezoelectric wafer (OPW) and TIDT, demonstrating a significant reduction of 48.82 % in stray S0 mode amplitude at 70 kHz and 94.77 % in stray A2 mode amplitude at 400 kHz. This method ensures high SNR for target mode signals, simplifying feature extraction and enhancing structural health monitoring capabilities.

Influence of temperature and preload force on capacitance and electromechanical impedance of lead zirconate titanate piezoelectric wafer active sensors for structural health monitoring of bolts

This study focuses on the effect of temperature and preload force on capacitance and electromechanical impedance of lead zirconate titanate piezoelectric wafer active sensors for structural health monitoring of bolts. We explain the influence of temperature on the basis of the phenomenological thermodynamic theory of ferroelectricity by Landau, Ginsburg and Devonshire. The article illustrates the effect of damping the radial deformation of piezoelectric sensors on the capacitance and electromechanical impedance spectra in structural health monitoring of bolts. We also explains the similarities between the effects of temperature and preload force on the electromechanical impedance spectra. We establish a clear correlation between the mechanical strain in the region of the sensor (here due to a preload force), the capacitance and the electromechanical impedance spectra and thus show that piezoelectric sensors made of lead–zirconate–titanate can be used excellently in areas of variable mechanical strain. The article enhances the understanding of the measurement method and facilitates the transfer of the measurement method to other problems in structural health monitoring. Furthermore, the acquired knowledge serves as a solid basis for verifying the plausibility of data sets containing electromechanical impedance spectra.

Durability Assessment of Bonded Piezoelectric Wafer Active Sensors for Aircraft Health Monitoring Applications.

This study conducted experimental and numerical investigations on piezoelectric wafer active sensors (PWASs) bonded to an aluminum plate to assess the impact of bonding degradation on Lamb wave generation. Three surface-bonded PWASs were examined, including one intentionally bonded with a reduced adhesive to create a defective bond. Thermal cyclic aging was applied, monitoring through laser Doppler vibrometry (LDV) and static capacitance measurements. The PWAS with the initially defective bond exhibited the poorest performance over aging cycles, emphasizing the significance of the initial bond condition. As debonding progressed, modifications in electromechanical behavior were observed, leading to a reduction in wave amplitude and distortion of the generated wave field, challenging the validity of existing analytical modeling of wave-tuning curves for perfectly bonded PWASs. Both numerical simulations and experimental observations substantiated this finding. In conclusion, this study highlights the imperative of a high-integrity bond for the proper functioning of a guided wave-based structural health monitoring (SHM) system, emphasizing ongoing challenges in assessing SHM performance.

Numerical Simulation of Lamb Wave Propagation in Ice

Lamb wave is one of the most used ultrasonic guided waves, which has a wide application prospect in the field of aircraft icing detection. The platform of Lamb wave propagation based on piezoelectric wafer was used as the physical model, and COMSOL software was used as the numerical calculation tool to simulate the propagation of Lamb wave in different thickness, length and types of ice. The results showed that the attenuation of voltage amplitude increases with the thickness of ice in a certain range. The time delay of Lamb wave B1 mode at the receiving end increases linearly with the increase of ice length. As the mechanical parameters of ice layer decrease, the attenuation of voltage amplitude and the time delay of Lamb wave B1 mode increase gradually. The propagation characteristics of Lamb wave in ice are preliminarily proved, which provides a theoretical reference for the subsequent propagation experiments of Lamb wave in ice.

Real-Time Structural Damage Detection Using EMI under Varying Load a and Temperature Conditions

Structural Health Monitoring (SHM) is a critical aspect of maintaining the safety and integrity of infrastructure. In recent years, there has been a significant shift towards adopting innovative techniques, and one of the most promising methods is Electromechanical Impedance (EMI). EMI involves the utilization of piezoelectric transducers to assess the health of structures in real-time by examining changes in their electrical characteristics. The presence of load causes a change in structural stiffness, which alters the resonant characteristics of the structure. Understanding how external factors like load and temperature influence the electrical impedance of these sensors is essential for its reliable application in damage detection. This article presents an experimental and numerical study to investigate the effects of load and temperature on the electrical impedance of a piezoelectric sensor used in the electromechanical impedance (EMI) technique. The experimental setup uses an impedance analyzer (Agilent 4294A model) to measure the in-situ EMI of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. The numerical model uses ANSYS software to simulate an aluminum beam at varying temperatures. The results show that the load and temperature have a significant effect on the impedance of the transducer. However, it is shown that it is still possible to detect damage using EMI even under varying load and temperature conditions. The results also show that the accuracy of EMI-based damage detection can be improved by using temperature and load compensation techniques.

A novel coupling method for ultrasonic transducer based on pressureless sintering of nano-Ag

The acoustic performance of ultrasonic transducer is directly determined by the coupling between the piezoelectric unit and the medium. At present, the coupling methods of the transducer are relatively single. In this work, a novel ultrasonic transducer coupling method based on pressureless sintering of nano-Ag is reported. The effects of sintering temperature on the micro-morphology and macro-porosity of nano-Ag sintered bulk (i.e. demoulded coupling layer) are systematically investigated. The transducers are design and fabricated by one-time pressureless sintering of nano-Ag paste. The influences of sintering temperature on acoustic performance of the transducers are analysed by experiments in detail. The results show that the number of the pores in the pressureless sintered bulk decrease with the sintering temperature, while the pores size gradually increase. From the perspective of acoustic impedance matching, silver materials have great advantages and universality as the coupling layer between most piezoelectric wafers and steel mediums. The coupling effect of sintered Ag could be improved by increasing the temperature. The best coupling effect could be obtained by sintering at 350 °C, at which the primary echo of the transducer has ideal waveform and bandwidth.

A novel pulse-echo piezoelectric transducer for detecting quasi-static component induced by an ultrasonic longitudinal wave

For effectively detecting the quasi-static component (QSC) of an ultrasonic longitudinal wave, which is closely related to the elastic nonlinearity of material, we proposed a novel pulse-echo piezoelectric transducer consisting of a high-frequency piezoelectric wafer, a frequency selective isolation layer, a low-frequency piezoelectric wafer, and an acoustic backing. The high-frequency wafer generates the primary longitudinal wave (PLW) tone burst, while the high- and low-frequency wafers receive the pulse echo containing both the PLW component and QSC, respectively. We analyze the pulse-echo formation of the high-frequency PLW tone burst in a specimen, and conduct numerical simulations and experiments to validate the effectiveness of the proposed transducer. The results demonstrate that the low-frequency receiver is more efficient at detecting the QSC, even though the high-frequency wafer can also receive the echo of the QSC pulse. Specifically, the QSC pulse can still be detected by the low-frequency receiver when the high-frequency PLW tone burst is completely attenuated. The novel pulse-echo transducer proposed in this paper expands the design perspectives for transducers used in ultrasonic non-destructive testing.

AI-enabled crack-length estimation from acoustic emission signal signatures

Abstract This article addresses the classification of fatigue crack length using artificial intelligence (AI) applied to acoustic emission (AE) signals. The AE signals were collected during fatigue of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-micron wide slit of various lengths. Fatigue testing was performed under stress-intensity-factor control to moderate crack advancement. The slit specimen produced AE signals only from crack advancement at the slit tips whereas the 1-mm hole specimens produced AE signals both from crack tip advancement and crack rubbing or clapping. The AE signals were captured with a piezoelectric wafer active sensor (PWAS) array connected to MISTRAS instrumentation and AEwin software. The collected AE signals were preprocessed using time-of-flight filtering and denoising. Choi Williams transform converted time-domain AE-signals into spectrograms. To apply machine learning, the spectrogram images were used as input data for the training, validation, and testing of a GoogLeNet convolutional neural network (CNN). The CNN was trained to sort the AE signals into crack-length classes. CNN performance enhancements, including synthetic data generation and class balancing were developed. A three-class example with crack lengths of (i) 10-12 mm; (ii) 12-14 mm; and (iii) 14-16 mm is provided. Our AI approach was able to classify the AE signals into these three classes with 91% accuracy thus proving that the AE signals contain sufficient information for crack estimation using an AI-enabled approach.

Implementation and displacement self-sensing of a 4-DOF piezoelectric micromanipulator

This paper reports the development and displacement self-sensing of a new four degrees-of-freedom (DOF) piezoelectric micromanipulator. In particular, each micromanipulator arm can move along the clamping and vertical directions, constructing with two degrees of freedom, and two micromanipulator arms exhibit four degrees of freedom. Thus, it improves operational flexibility. Meanwhile, the self-sensing method avoids additional external sensors when detecting output displacements. The piezoelectric micromanipulator is devised based on the transverse inverse piezoelectric effect of two sets of vertical spatial crossings. Static and dynamic characteristics are analyzed via finite element optimization of geometric parameters. Then, a displacement self-sensing method based on charge integration is proposed. This method measures the generated charge on the clamping arm surface from the piezoelectric wafer deformation under the driving voltage. A self-sensing circuit is designed to obtain the surface charge of the clamping arm. Then, several experiments are conducted to test the micromanipulator. The maximum displacements of the designed micromanipulator are about 66.29 μm and 63.09 μm along clamping and vertical directions. Moreover, output displacements, response time, and inherent frequencies obtained based on the self-sensing method agree well with those with laser sensors. In addition, an operation application for micro-shaft assembly is presented, demonstrating the grasping and docking operations of the micro-shaft in the clamping and vertical directions.