• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.366-372
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• 2007

Self-sensing measurement of piezo inkjet and its application are discussed. The pressure wave inside the inkjet dispenser was measured by current measurement due to self-sensing capability of PZT. The pressure wave measured from current was verified by commercially available laser vibrometer. Here, two applications using self-sensing signal were discussed: waveform design for high speed jetting and condition monitoring. For waveform design, two pulse waveform was designed based on self-sensing signal such that the pressure wave after droplet formation can be minimized. For condition monitoring, self-sensing signal was shown to be effective in detecting air bubble trapped in inkjet printhead.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.4 s.121
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• pp.333-341
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• 2007

Waveform design method for inkjet printing has been proposed tv pressure wave measurement. The pressure wane inside the inkjet dispenser can be effectively measured by current measurement due to self-sensing capability of PZT. The pressure wave measured from current was verified by commercially availablelaser vibrometer. In order to obtain high speed inkjet droplets, two pulse waveform was designed such that the pressure wane after droplet formation can be minimized.

• Journal of Sensor Science and Technology
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• v.19 no.2
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• pp.124-129
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• 2010

For the industrial printing applications, the stability of the piezo-driven inkjet printhead is a major requirement. In this paper, we focused on the failure modes of the inkjet printhead and realized a method to detect and repair them at high speed. The printhead monitoring is performed by detecting the residual vibration of the actuating plate using the self- sensing capability of the piezoelectric material. To measure the channel acoustics and to identify the malfunctioning nozzle, we devised the bridge sensing circuitry and failure detection algorithm. The residual vibration signals can be affected by the boundary conditions of the channel acoustics, so it is possible to identify the failure causes by analyzing the monitoring signals. Therefore it is also possible to apply a proper restoring process to the defective printhead. The experimental results show that this method is effective in improving the reliability of the industrial printing.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.562-565
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• 2008

Measuring system for piezo inkjet droplet and ink jetting status was developed. In order to measure the jetting performance, two different approaches have been used. One is to use the self-sensing capability of the piezo actuator in the printhead. The other is to use the droplet image from a CCD camera. The software as well as hardware using two approaches was developed. The self-sensing signal as well as meniscus motion was shown to be useful in monitoring jetting conditions. Furthermore, ink properties such as viscosity and speed of sound, which is related to jetting performance, can be understood.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.455-460
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• 2007

A self-sensing circuit for piezo inkjet has been designed in order to monitor the operating condition during printing. In order to verify the circuit, both ink droplet images from strobe LED and vibration signals from the laser vibrometer were measured and compared with self-sensing signal. Experimental results show that self-sensing signal was effective in detecting the pressure wave change due to the bubble trapped in inkjet printhead.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.757-758
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• 2009

• Smart Structures and Systems
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• v.6 no.2
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• pp.167-182
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• 2010

An adaptronic strut for machine tools with parallel kinematics for compensation of the influence of geometric errors is introduced. Implemented within the strut is a piezoelectric sensor-actuator unit separated in function. In the first part of this contribution, the functional principle of the strut is presented. For use of one piezoelectric transducer as both, sensor and actuator as so-called self-sensing actuator, the acquisition of the sensing signal while actuating simultaneously using electrical bridge circuits as well as filter properties are examined. In the second part the control concept developed for the adaptronic strut is presented. A co-simulation model of the strut for simulating the controlled multi-body behavior of the strut is set-up. The control design for the strut as a stand-alone system is tested under various external loads. Finally, the strut is implemented into a model of the complete machine tool and the influence of the controlled strut onto the behavior of the machine tool is examined.

• Smart Structures and Systems
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• v.8 no.3
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• pp.275-302
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• 2011

This is a review paper on mathematical modeling of actively controlled piezo smart structures. Paper has four sections to discuss the techniques to: (i) write the equations of motion (ii) implement sensor-actuator design (iii) model real life environmental effects and, (iv) control structural vibrations. In section (i), methods of writing equations of motion using equilibrium relations, Hamilton's principle, finite element technique and modal testing are discussed. In section (ii), self-sensing actuators, extension-bending actuators, shear actuators and modal sensors/actuators are discussed. In section (iii), modeling of thermal, hygro and other non-linear effects is discussed. Finally in section (iv), various vibration control techniques and useful software are mentioned. This review has two objectives: (i) practicing engineers can pick the most suitable philosophy for their end application and, (ii) researchers can come to know how the field has evolved, how it can be extended to real life structures and what the potential gaps in the literature are.

• Smart Structures and Systems
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• v.5 no.2
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• pp.139-152
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• 2009

Globally, civil infrastructures are deteriorating at an alarming rate caused by overuse, overloading, aging, damage or failure due to natural or man-made hazards. With such a vast network of deteriorating infrastructure, there is a growing interest in continuous monitoring technologies. In order to provide a true distributed sensor and control system for civil structures, we are developing a Structural Nervous System that mimics key attributes of a human nervous system. This nervous system is made up of building blocks that are designed based on mechanoreceptors as a fundamentally new approach for the development of a structural health monitoring and diagnostic system that utilizes the recently developed piezo-fibers capable of sensing and actuation. In particular, our research has been focused on producing a sensory nervous system for civil structures by using piezo-fibers as sensory receptors, nerve fibers, neuronal pools, and spinocervical tract to the nodal and central processing units. This paper presents up to date results of our research, including the design and analysis of the structural nervous system.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.301-306
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• 2022

With the recent widespread implementation of Internet of Things (IoT) technology driven by Industry 4.0, self-powered sensors for wearable and implantable systems are increasingly gaining attention. Piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs), which convert biomechanical energy into electrical energy, can be considered as efficient self-powered sensor platforms. These are energy harvesters that are used as low-power energy sources. However, they can also be used as sensors when an output signal is used to sense any mechanical stimuli. For sensors, collecting high-quality data is important. However, the accuracy of sensing for practical applications is equally important. This paper provides a brief review of the performance advanced by the materials and structures of the latest PENG/TENG-based wearable sensors and intelligent applications applied using artificial intelligence (AI)