Enhancement of sensitivity of magnetostrictive foam in low magnetic fields for sensor applications

Norhaniza, Rizuan; Mazlan, Saiful Amri; Ubaidillah, Ubaidillah; Aziz, Siti Aishah Abdul; Nazmi, Nurhazimah; Yunus, Nor Alafiza

doi:10.1016/j.polymer.2020.123083

Cited by 17 publications

(8 citation statements)

References 32 publications

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“…The MR foam was fabricated based on the composition done and published by Norhaniza et al 19 Flexible polyurethane (PU) was used as the polymer matrix, where PU polyol and diisocyanate were obtained from Smooth On Inc. The magnetic particles, type of carbonyl iron particles (CIPs) with an average size of 3–5 um were purchased from CK Materials Lab Co Ltd.…”

Section: Methodsmentioning

confidence: 99%

“…The magnetic particles, type of carbonyl iron particles (CIPs) with an average size of 3–5 um were purchased from CK Materials Lab Co Ltd. The composition concentrations of CIP are stated in Table 4 whereas the ranges have high magnetic saturation and low remanence values of MR foam 19 …”

Section: Methodsmentioning

confidence: 99%

“…The MR foam turns into black color after the addition of CIP. From the naked eye, the color is fair in all areas of the MR foam showing even distribution of CIPs 19 . The morphology study is done using a scanning electron microscope (SEM) brand JEOL JSM‐IT300LV.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, a machine learning‐based model to predict the magnetostriction effect on a new MPC material, which is MR foam is introduced in this study. The material is chosen because of its high response to a low magnetic field, low remanence, and high sensitivity for sensor and actuator applications required in micro‐scale sensitivity 19 . Additionally, strain and normal force are the important parameters in the MR foam that needs to be predicted especially when they are dependent on the concentration of magnetic particles and magnetic field intensity 20 …”

Section: Introductionmentioning

confidence: 99%

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Prediction for magnetostriction magnetorheological foam using machine learning method

Rohim

Nazmi

Bahiuddin

et al. 2022

J of Applied Polymer Sci

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Magnetorheological (MR) foam is a magnetic polymer composite (MPC) that can be used for soft sensors and actuators in soft robotics. Modeling mechanical properties and magnetostriction behavior of MR foam is critical to developing into MR foam devices. This study uses extreme learning machines (ELM) and artificial neural networks (ANN) to predict magnetostriction behavior.These models describe the nonlinear relationship between different carbonyl iron particle compositions, magnetic field, strain, and normal force. The model's hyperparameters (learning algorithms and activation functions) are varied. For ANN, RMSProp, and ADAM learning algorithms were used with

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction for magnetostriction magnetorheological foam using machine learning method

Rohim

Nazmi

Bahiuddin

et al. 2022

J of Applied Polymer Sci

Self Cite

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“…Digital microscopes or cameras were commonly employed to carry out direct measurement of magnetic displacement of ME material. [10][11][12][13] However, they are limited in resolution when it comes to magnetic deflection or deformation within sub-micrometre scale. Electrical techniques, including strain gauge are relatively more accurate and low-cost.…”

Section: Introductionmentioning

confidence: 99%

Low‐Field Actuating Magnetic Elastomer Membranes Characterized using Fibre‐Optic Interferometry

Li,

Coote,

Subburaman

et al. 2023

Adv Funct Materials

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Smart robotic devices remotely powered by magnetic field have emerged as versatile tools for wide biomedical applications. Soft magnetic elastomer (ME) composite membranes with high flexibility and responsiveness are frequently incorporated to enable local actuation for wireless sensing or cargo delivery. However, the fabrication of thin ME membranes with good control in geometry and uniformity remains challenging, as well as the optimization of their actuating performances under low fields (milli‐Tesla). In this work, the development of ME membranes comprising of low‐cost magnetic powder and highly soft elastomer through a simple template‐assisted doctor blading approach, is reported. The fabricated ME membranes are controllable in size (up to centimetre‐scale), thickness (tens of microns) and high particle loading (up to 70 wt.%). Conflicting trade‐off effects of particle concentration upon magnetic responsiveness and mechanical stiffness are investigated and found to be balanced off as it exceeds 60 wt.%. A highly sensitive fibre‐optic interferometric sensing system and a customized fibre‐ferrule‐membrane probe are first proposed to enable dynamic actuation and real‐time displacement characterization. Free‐standing ME membranes are magnetically excited under low field down to 2 mT, and optically monitored with nanometer accuracy. The fast and consistent responses of ME membranes showcase their promising biomedical applications in nanoscale actuation and sensing.

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Different Structural Configurations of Magnetostrictive Energy Harvester: A Comprehensive Review for Sensor Applications

Dolui,

Dasadhikari,

Bose

et al. 2024

Lecture Notes in Electrical Engineering

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Enhancement of sensitivity of magnetostrictive foam in low magnetic fields for sensor applications

Cited by 17 publications

References 32 publications

Prediction for magnetostriction magnetorheological foam using machine learning method

Prediction for magnetostriction magnetorheological foam using machine learning method

Low‐Field Actuating Magnetic Elastomer Membranes Characterized using Fibre‐Optic Interferometry

Different Structural Configurations of Magnetostrictive Energy Harvester: A Comprehensive Review for Sensor Applications

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