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Search Results (2)

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Keywords = remanence inspection

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20 pages, 13158 KiB  
Article
Nondestructive Detection of Magnetic Contaminant in Aluminum Casting Using Thin Film Magnetic Sensor
by Tomoo Nakai
Sensors 2021, 21(12), 4063; https://doi.org/10.3390/s21124063 - 12 Jun 2021
Cited by 9 | Viewed by 2610
Abstract
The thin film magneto-impedance sensor is useful for detecting a magnetic material nondestructively. The sensor made by single layer uniaxial amorphous thin film has a tolerance against surface normal magnetic field because of its demagnetizing force in the thickness direction. Our previous study [...] Read more.
The thin film magneto-impedance sensor is useful for detecting a magnetic material nondestructively. The sensor made by single layer uniaxial amorphous thin film has a tolerance against surface normal magnetic field because of its demagnetizing force in the thickness direction. Our previous study proposed the sensitive driving circuit using 400 MHz high frequency current running through the sensor to detect the logarithmic amplifier. We also confirmed the sensitivity of the sensor within 0.3 T static normal magnetic field, which resulted in detection of 5 × 10−8 T of 5 Hz signal. This paper proposes a nondestructive inspection system for how detecting a contaminant of small tool steel chipping in aluminum casting specimen would be carried out. Three channel array sensors installed in the 30 mT static field detecting area were fabricated and experimentally showed a detection of low remanence magnetic contaminant in a bulk aluminum casing specimen. Full article
(This article belongs to the Special Issue Magnetic Sensor and Its Applications)
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Figure 1

Figure 1
<p>Conceptual image of development.</p> Full article ">Figure 2
<p>Fabrication process flow of sensor.</p> Full article ">Figure 3
<p>Fabricated sensors on glass substrate.</p> Full article ">Figure 4
<p>Schematic of a demerit of differential sensor.</p> Full article ">Figure 5
<p>Measurement system of sensor element impedance.</p> Full article ">Figure 6
<p>Typical impedance variation of the sensor based on <span class="html-italic">Z</span> = <span class="html-italic">R</span> + <span class="html-italic">Xi</span>; (<b>a</b>) Impedance <span class="html-italic">Z</span>; (<b>b</b>) Resistance <span class="html-italic">R</span>; (<b>c</b>) Reactance <span class="html-italic">X</span>.</p> Full article ">Figure 7
<p>Typical variation of sensitivity dZ/dH of the sensor.</p> Full article ">Figure 8
<p>Impedance variation of sensors used in the prototype system; (<b>a</b>) Sensor 1; (<b>b</b>) Sensor 2; (<b>c</b>) Sensor 3.</p> Full article ">Figure 9
<p>View of sensor head unit.</p> Full article ">Figure 10
<p>View of evaluation unit of the sensor unit performance.</p> Full article ">Figure 11
<p>Variations of the output of the sensor unit; (<b>a</b>) Sensor 1; (<b>b</b>) Sensor 2; (<b>c</b>) Sensor 3.</p> Full article ">Figure 12
<p>Schematic of measurement system.</p> Full article ">Figure 13
<p>Photo of fabricated measurement system.</p> Full article ">Figure 14
<p>Measured distribution of magnetic flux density of the system; (<b>a</b>) Color map; (<b>b</b>) Contour diagram.</p> Full article ">Figure 15
<p>Whole view of the developed prototype system.</p> Full article ">Figure 16
<p>Schematic of measured sample; (<b>a</b>) Dimensions drawing; (<b>b</b>) Photograph.</p> Full article ">Figure 17
<p>Photo of measurement while feeding.</p> Full article ">Figure 18
<p>Magnetization-loop of the tool steel chipping.</p> Full article ">Figure 19
<p>Feeding position of the tool steel chipping.</p> Full article ">Figure 20
<p>Estimated profile of sensor signal for three sensors in different positions.</p> Full article ">Figure 21
<p>Measured profile of sensor signal obtained from the prototype system.</p> Full article ">Figure 22
<p>Variation of output range as a function of feeding position.</p> Full article ">Figure A1
<p>View of sensor element in 2016 (original).</p> Full article ">Figure A2
<p>View of sensor element in 2020 (after 4 years).</p> Full article ">Figure A3
<p>Comparison of sensor property of 400 MHz between the origin and then after 4 years; (<b>a</b>) Impedance variation; (<b>b</b>) Resistance variation; (<b>c</b>) Reactance variation.</p> Full article ">
3217 KiB  
Article
Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing
by Juwei Zhang and Xiaojiang Tan
Sensors 2016, 16(9), 1366; https://doi.org/10.3390/s16091366 - 25 Aug 2016
Cited by 40 | Viewed by 8793
Abstract
Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional [...] Read more.
Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision. Full article
(This article belongs to the Special Issue Giant Magnetoresistive Sensors)
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Figure 1

Figure 1
<p>(<b>a</b>) Framework of the detection device and detection method diagram; (<b>b</b>) Excitation source; and (<b>c</b>) Signal acquisition system board.</p> Full article ">Figure 2
<p>(<b>a</b>) Unrolled detection surface; (<b>b</b>) Raw data rolling image.</p> Full article ">Figure 3
<p>Signal preprocessing flowchart.</p> Full article ">Figure 4
<p>Three different sensor channels of raw MFL data.</p> Full article ">Figure 5
<p>Unrolled preprocessed signals.</p> Full article ">Figure 6
<p>CSWF filtered three-dimensional diagram.</p> Full article ">Figure 7
<p>Three-dimensional diagram interpolated data.</p> Full article ">Figure 8
<p>Photos (<b>above</b>) and local MFL image (<b>below</b>).</p> Full article ">Figure 9
<p><span class="html-italic">L1</span> and <span class="html-italic">L2</span> sketch map.</p> Full article ">Figure 10
<p>Different hidden nodes recognition graphs: (<b>a</b>) Identification ratio graph of 21 hidden layer nodes; (<b>b</b>) Identification ratio graph of 24 hidden layer nodes; (<b>c</b>) Identification ratio graph of 27 hidden layer nodes; and (<b>d</b>) Identification ratio graph of 30 hidden layer nodes.</p> Full article ">Figure 11
<p>Training performance graphs for different hidden layer numbers: (<b>a</b>) 21 hidden nodes; (<b>b</b>) 24 hidden nodes; (<b>c</b>) 27 hidden nodes; and (<b>d</b>) 30 hidden nodes.</p> Full article ">
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