• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.45-50
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• 2020

Magnetoelectric (ME) composites were designed using the PMN-PZT single crystal and Ni foils; the properties and magnetic-field sensitivities of ME composites with different piezoelectric vibration modes (i.e., 31, 32, and 36 modes that depend on the crystal orientation of the single crystal) were compared. In the off-resonance condition, the ME coupling properties of the ME composites with the 32 and 36 piezoelectric vibration modes were better than those of the ME composites with the 31 piezoelectric vibration mode. However, in the resonance condition, the ME coupling properties of the ME composites were almost similar, irrespective of the piezoelectric vibration mode. Additionally, in the off-resonance condition (at 1 kHz), the magnetic-field sensitivity of the ME composites with the 36 piezoelectric vibration mode was up to 2 nT and those of the ME composites with the 31 and 32 piezoelectric vibration modes were up to 5 nT. These magnetic-field sensitivities are similar to those offered by conventional high-sensitivity magnetic-field sensors; the potential of the proposed sensor to replace costly and bulky high-sensitivity magnetic field sensors is significant.

• Journal of Korean Association for Spatial Structures
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• v.6 no.3 s.21
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• pp.35-41
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• 2006

Various monitoring sensors have been used for the monitoring, damage and vibration prediction of structures. They have been used for sensing damage in a variety of materials and structures such as piezoelectric materials (PZT) and electric strain gauges. But, many experiments of vibration were not performed. The PZT changes physical force if load cell to electrical signal due to deformation of structure. The voltage change of piezoelectric sensors for plates are used for vibration prediction. In this study, a fundamental study for vibration prediction using piezoelectric sensors are discussed in plates.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.6
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• pp.669-677
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• 2012

This study presents a method to monitor cable force using wireless sensor nodes and piezoelectric sensors. The following approaches are carried out to achieve the objective. Firstly, the principle of piezoelectric materials (e.g., PZT) as strain sensors is reviewed. A cable force estimation method using dynamic features of cables measured by piezoelectric materials is presented. Secondly, the design of an automated cable force monitoring system using the data acquisition sensor-node Imote2/SHM-DAQ is described. The sensor node is originally developed by University of Illinois at Urbana-Champaign and is adopted in this study to monitor strain-induced voltage from PZT sensors. The advantages of the system are cheap, and eligible for wireless communication and automated operation. Finally, the feasibility of the proposed monitoring system is evaluated on a lab-scaled cable.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.5
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• pp.354-360
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• 1999

PCN-PZT piezoelectric acceleration sensors of annular shear mode voltage type were fabricated and their characteristics have been investigated. Field tests are also carried out. To avoid noise problems from the environmental conditions, acceleration sensors employed solid state micro-electronics for pre-amplifier. The calibration procedures based on the principle of the comparison method were adopted for investigating the characteristics of fabricated acceleration sensors. The voltage sensitivity and resonant frequency of fabricated acceleration sensors were 83mv/g, 23kHz, respectively. The lower and upper frequency limit were 4Hz and 9kHz, respectively. The variation of the voltage sensitivity showed 10% at $-406{\circ}C\; and\; 9%\; at\; 121^{\circ}C$ compared to that of reference temperature at $40^{\circ}C$.

• Journal of Sensor Science and Technology
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• v.19 no.3
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• pp.202-208
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• 2010

This study examined the dependency of crystalline orientation and electric properties of sol-gel PZT film on hydrolysis, a $PbTiO_3$ seed layer and a concentration of sol-gel solution. The PZT thin films were prepared by using 2-Methoxyethanol-based sol-gel method and spin-coating on Pt/Ti/$SiO_2$/Si substrates. The 1-${\mu}m$-thick PZT films were coated and then fired in a furnace by direct insert method. The highly (111) oriented PZT film of pure perovskite structure could be obtained. We could control the degree of orientation by various parameters such as hydrolysis, a $PbTiO_3$ seed layer and a concentration of sol-gel solution. The highest measured remanent polarization, dielectric constant and piezoelectric coefficient are $24.16\;{\mu}C/cm^2$, 2808, and 159 pC/N, respectively.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.5
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• pp.390-396
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• 2014

A study on smart seats for railroad vehicles was conducted using piezoelectric (PZT) sensors. For this purpose, the concept of passenger friendly smart seats was defined, and a PZT sensor was selected as the optimum sensor based on this concept. Using PZT sensors, simulation tests were performed using a sub-scale model railroad vehicle. In these tests, the main functions of the smart seats were extracted and simplified to improve the effectiveness of the simulation tests. Based on the test results, the system for smart seats proposed in this paper was successfully verified using PZT sensors and the dedicated operation software for the system. This paper will contribute to the improvement of services in high-speed rail systems through advances in ticket checking tasks.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.125-128
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• 2003

Low-velocity impact damage is a major concern in the design of structures made of composite materials, because impact damage is hidden inside and cannot be detected by visual inspection. The piezoelectric thin film sensor can be used to detect variations in structural and material properties for structural health monitoring. In this paper, the PVDF and PZT sensors were used for monitoring impact damage initiation in Gr/Ep composite panel to illustrate this potential benefit. A series of impact test at various impact energy by changing impact mass and height is performed on the instrumented drop weight impact tester. The wavelet transform(WT) is used to decompose the piezoelectric sensor signals in this study. Test results show that the particular waveform of sensor signals implying the damage initiation and development are detected above the damage initiation impact energy. And it is found that both PZT and PVDF sensors can be used to detect the impact damage.

• Journal of the Korean Society for Nondestructive Testing
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• v.12 no.4
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• pp.9-17
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• 1993

Three types of piezoelectric sensors to detect acoustic emission signals were developed and characterized. Epicentral displacement and velocity of a plate to have infinite boundary were calculated by convolution between a Green's function and a simulated source time function to show parabolic rising characteristic. The sensor calibration system set up was composed of a steel plate, a glass capillary, an indentor and a load cell indicator The transient elastic signals were detected by the sensors. The results were compared with the theoretical results and Fast Fourier Transformed. As the results, the sensor fabricated using a disk shape of a piezoelectric PZT element showed resonant characteristics. The sensors fabricated using a conical shape PZT element and a PVDF polymer film showed the wide band characteristics for particle displacement and velocity, respectively. The calculated results showed good agreements with the transient responses in the cases of the wide band sensors and it was confirmed that the simulated source time function had been properly assumed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.03b
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• pp.13-13
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• 2010

Piezoelectric sensors are extensively used to measure force because of their high sensitivity and low cost. however, the development of device with reduced size but with improved sensitivity is highly important. Low-temperature co-fired ceramic (LTCC) is one of promising materials for this application than a silicon substrate because it has very good electrical and mechanical properties as well as possibility of making various three dimensional (3D) structures. In this work, piezoelectric pressure sensors based on hybrid LTCC technology were presented. The LTCC diaphragms with thickness of $400\;{\mu}m$ were fabricated by laminating 12 green tapes which consist of alumina and glass particle in an organic binder. The piezoelectric sensing layer consists of PZT thin film deposited by RF magnetron sputtering method on between top and bottom Au electrodes. The PZT films deposited on LTCC diaphragms were successfully grown and were analyzed by using X-ray diffraction method (XRD) and field emission scanning electron microscope (FESEM).

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.