Measurement of Shear Strain Field in a Soft Material Using a Sensor System Consisting of Distributed Piezoelectric Polymer Film

Li, Fengyu; Akiyama, Yasuhiro; Wan, Xianglong; Okamoto, Shogo; Yamada, Yoji

doi:10.3390/s20123484

Cited by 9 publications

(10 citation statements)

References 28 publications

(25 reference statements)

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“…Note that the contact part did not slip on the substrate, and the surface motion strictly agreed with the contact plate. The signal conditioning method was developed using an applicable charge amplifier circuit to obtain a real-time charge output with high signal-to-noise ratio [21]. The amplifier provides stable signal amplification in a wide frequency range up to 100-Hz, so that the influence of phase shift induced by the vibration can be neglected.…”

Section: Experiments and Data Processing Methods A Experimental Setupmentioning

confidence: 99%

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Built-in Sensor System for Monitoring Internal Shear Strain and Stress Distribution in Soft Materials

Akiyama

Wan

et al. 2022

IEEE Access

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Soft robotics requires flexible sensors that can realize sensitive measurements in which rigid sensors are not viable. Every sensor requires a precise and quantitative measurement of the mechanical phenomena; however, in the field of soft robotics, the complex structure and physical properties of composite materials complicate the construction of a precise sensing model. A built-in sensor system for internal shear strain and stress distribution measurement was developed by embedding piezoelectric polyvinylidene fluoride polymer films inside an objective soft material. The shear strain sensing models were established by relating piezoelectricity and cantilever bending mechanics. For validation, the deflection of the embedded film sensors was monitored and digitized using a video camera in experiments. The shear strain sensing model was validated by evaluating the agreement between the modeled and measured physical variations with a high coefficient of determination of 0.974. Based on the linear relation of arithmetic expression of each sensing model, the calibrated results for internal shear strain and stress sensing were also successfully obtained. This paper presents a discussion of the measurement effect of this type of flexible sensor and its potential for further applications.

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Section: Experiments and Data Processing Methods A Experimental Setupmentioning

confidence: 99%

“…In a previous study, Li et al developed a sensor system for measuring the internal shear strain distribution for soft materials by embedding an array of polyvinylidene fluoride (PVDF) films [21], [22]. PVDF is a thin film-type piezoelectric polymer [23].…”

Section: Introductionmentioning

confidence: 99%

Built-in Sensor System for Monitoring Internal Shear Strain and Stress Distribution in Soft Materials

Akiyama

Wan

et al. 2022

IEEE Access

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“…It is a well-known fact that a combination of different mechanical parameters is crucial for the development of soft sensor technology, particularly in terms of soft sensing capabilities [ 17 , 18 , 19 ]. Therefore, this study presented a combination of different mechanical performances and sensitivities of MA foam in terms of various physical capabilities such as magnetostriction, normal force, storage modulus, shear stress and torque.…”

Section: Introductionmentioning

confidence: 99%

Sensitivities of Rheological Properties of Magnetoactive Foam for Soft Sensor Technology

Norhaniza

Mazlan

Ubaidillah

et al. 2021

Sensors

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Magnetoactive (MA) foam, with its tunable mechanical properties and magnetostriction, has the potential to be used for the development of soft sensor technology. However, researchers have found that its mechanical properties and magnetostriction are morphologically dependent, thereby limiting its capabilities for dexterous manipulation. Thus, in this work, MA foam was developed with additional capabilities for controlling its magnetostriction, normal force, storage modulus, shear stress and torque by manipulating the concentration of carbonyl iron particles (CIPs) and the magnetic field with regard to morphological changes. MA foams were prepared with three weight percentages of CIPs, namely, 35 wt.%, 55 wt.% and 75 wt.%, and three different modes, namely, zero shear, constant shear and various shears. The results showed that the MA foam with 75 wt.% of CIPs enhanced the normal force sensitivity and positive magnetostriction sensitivity by up to 97% and 85%, respectively. Moreover, the sensitivities of the storage modulus, torque and shear stress were 8.97 Pa/mT, 0.021 µN/mT, and 0.0096 Pa/mT, respectively. Meanwhile, the magnetic dipolar interaction between the CIPs was capable of changing the property of MA foam from a positive to a negative magnetostriction under various shear strains with a low loss of energy. Therefore, it is believed that this kind of highly sensitive MA foam can potentially be implemented in future soft sensor systems.

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“…The authors in [ 23 ] presented a new approach to LiDAR (light detection and ranging) modeling in a virtual test environment, where the sensor was replaced by a sensor model for the development and validation of reliable environmental perception systems for automated driving functions. To determine the properties of materials, the authors in [ 24 ] used a shear stress sensor system consisting of distributed embedded piezoelectric polymer films. The shear stress sensing model was mathematically created.…”

Section: Introductionmentioning

confidence: 99%

Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

Nikitin

Božek

Peterka

2020

Sensors

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The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.

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Measurement of Shear Strain Field in a Soft Material Using a Sensor System Consisting of Distributed Piezoelectric Polymer Film

Cited by 9 publications

References 28 publications

Built-in Sensor System for Monitoring Internal Shear Strain and Stress Distribution in Soft Materials

Built-in Sensor System for Monitoring Internal Shear Strain and Stress Distribution in Soft Materials

Sensitivities of Rheological Properties of Magnetoactive Foam for Soft Sensor Technology

Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

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