scholarly journals Impact of electromagnetic interface of soft magnetic sensor on IoTs current detector: Design and development

2019 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Hsu Chang-Hung ◽  
Chang Ann-Shing ◽  
Fung Chin-Ping
Keyword(s):  
Magnetic Sensor ◽  
Soft Magnetic ◽  
Design And Development ◽  
Detector Design

scholarly journals Design and development of ITER high-frequency magnetic sensor

2016 ◽  
Vol 112 ◽  
pp. 594-612 ◽  
Author(s):  
Y. Ma ◽  
G. Vayakis ◽  
L.B. Begrambekov ◽  
J.-J. Cooper ◽  
I. Duran ◽  
...  
Keyword(s):  
High Frequency ◽  
Magnetic Sensor ◽  
Design And Development

Novel Fabrication Process for the Integration of MEMS Devices with Thick Amorphous Soft Magnetic Field Concentrators

2007 ◽  
Vol 1052 ◽  
Author(s):  
Simon Brugger ◽  
Wilhelm Pfleging ◽  
Oliver Paul
Keyword(s):  
Magnetic Field ◽  
Laser System ◽  
Magnetic Sensor ◽  
Fabrication Process ◽  
Magnetic Flux Density ◽  
Mems Devices ◽  
Integration Process ◽  
Soft Magnetic ◽  
Micro Patterning ◽  
Field Concentrator

AbstractThis paper reports a novel fabrication process enabling the integration of mechanical MEMS devices with thick amorphous soft magnetic field concentrators. The integration process combines silicon on insulator technology for the MEMS device fabrication and epoxy-resin-based attachment of 18-µm-thick amorphous soft magnetic ribbons followed by a wet chemical structuring process. The fabrication process is reported on the basis of a field-concentrator-based resonant magnetic sensor combining an electrostatically driven micromechanical resonator and a planar magnetic field concentrator with two narrow gaps. For realization of the concentrator gaps, the integration process is extended by micro-patterning of the soft magnetic ribbons via UV-laser ablation using an excimer laser system. The characterization of the fabricated resonant magnetic sensor using a stroboscopic video microscope for in-plane motion measurement shows a high sensitivity of 390 kHz/T at a magnetic flux density of 158 µT.

scholarly journals Development of Magnetic Microwires for Magnetic Sensor Applications

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4767 ◽  
Author(s):  
Valentina Zhukova ◽  
Paula Corte-Leon ◽  
Mihail Ipatov ◽  
Juan Maria Blanco ◽  
Lorena Gonzalez-Legarreta ◽  
...  
Keyword(s):  
Magnetic Properties ◽  
Hysteresis Loops ◽  
Magnetic Sensor ◽  
Internal Stresses ◽  
Soft Magnetic ◽  
Magnetoelastic Anisotropy ◽  
Sensor Applications ◽  
Gmi Effect ◽  
Magnetic Softness ◽  
Magnetic Wires

Thin magnetic wires can present excellent soft magnetic properties (with coercivities up to 4 A/m), Giant Magneto-impedance effect, GMI, or rectangular hysteresis loops combined with quite fast domain wall, DW, propagation. In this paper we overview the magnetic properties of thin magnetic wires and post-processing allowing optimization of their magnetic properties for magnetic sensor applications. We concluded that the GMI effect, magnetic softness or DW dynamics of microwires can be tailored by controlling the magnetoelastic anisotropy of as-prepared microwires or controlling their internal stresses and domain structure by appropriate thermal treatment.

AGV Magnetic Sensor Data Acquisition System

2015 ◽  
Vol 713-715 ◽  
pp. 1056-1060 ◽  
Author(s):  
Wei Xin Wang
Keyword(s):  
Magnetic Field ◽  
Data Acquisition ◽  
Data Acquisition System ◽  
Magnetic Sensor ◽  
Acquisition System ◽  
Sensor Data ◽  
Magnetic Data ◽  
Soft Magnetic ◽  
The Magnetic Field ◽  
Sensor Data Acquisition

Based on PNI11096 magnetic sensor data acquisition system can be used for path recognition of automatic guided vehicle (AGV). The use of the magnetic field on the path to produce paving soft magnetic strip, AGV at run time through the sensor detects the current relative to the magnetic field strength of soft magnetic data will be returned in the STM32 chip data processing after two values through the open collector output 16 bit IO, provide location information for AGV.

scholarly journals Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 212
Author(s):  
Maohua Lin ◽  
Moaed A. Abd ◽  
Alex Taing ◽  
Chi-Tay Tsai ◽  
Frank D. Vrionis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensor Array ◽  
Learning Algorithms ◽  
Magnetic Sensor ◽  
Machine Learning Algorithms ◽  
Cervical Disc ◽  
Artificial Disc ◽  
Soft Magnetic ◽  
Human Spine ◽  
3D Printed

Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person’s spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.

Microstructure in soft magnetic E3 (S1, Al) (Sendust) alloys

1988 ◽  
Vol 46 ◽  
pp. 770-771
Author(s):  
June D. Kim
Keyword(s):  
Electron Microscopy ◽  
Magnetic Properties ◽  
Ternary Phase Diagram ◽  
Vertical Tube ◽  
Vacuum Induction Melting ◽  
Induction Melting ◽  
Soft Magnetic ◽  
Quenching Temperature ◽  
Thin Foils ◽  
Vertical Tube Furnace

Iron-base alloys containing 8-11 wt.% Si, 4-8 wt.% Al, known as “Sendust” alloys, show excellent soft magnetic properties. These magnetic properties are strongly dependent on heat treatment conditions, especially on the quenching temperature following annealing. But little has been known about the microstructure and the Fe-Si-Al ternary phase diagram has not been established. In the present investigation, transmission electron microscopy (TEM) has been used to study the microstructure in a Sendust alloy as a function of temperature.An Fe-9.34 wt.% Si-5.34 wt.% Al (approximately Fe3Si0.6Al0.4) alloy was prepared by vacuum induction melting, and homogenized at 1,200°C for 5 hrs. Specimens were heat-treated in a vertical tube furnace in air, and the temperature was controlled to an accuracy of ±2°C. Thin foils for TEM observation were prepared by jet polishing using a mixture of perchloric acid 15% and acetic acid 85% at 10V and ∼13°C. Electron microscopy was performed using a Philips EM 301 microscope.

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