Fast classification of non-magnetic metal targets using eddy-current based impedance spectroscopy

O’Toole, Michael D.; Karimian, Noushin; Peyton, Anthony

doi:10.1109/icsens.2017.8234219

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…We suggest that by comparing the two MIS components of the test piece as described, with one sensitive and the other insensitive to conductivity, we may account for the effects of size and shape of the test piece and classify its material using only magnetic induction measurements. This idea was initially proposed in previous work by the authors [22]. This work presented some preliminary results for a small set of manufactured samples (36 pieces in total) on a laboratorybased test rig.…”

Section: Classificationmentioning

confidence: 99%

Classification of Nonferrous Metals Using Magnetic Induction Spectroscopy

O’Toole

Karimian

Peyton

2018

IEEE Trans. Ind. Inf.

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Recycling automotive, electronic and other end-oflife waste liberates large quantities of metals which can be returned to the supply chain. Sorting the non-ferrous metals however, is not straightforward. Common methods range from laborious hand-sorting to expensive and environmentally deleterious wet processes. The goal is to move towards dry processes, such as induction sensors and vision systems, which can identify and sort non-ferrous scrap efficiently and economically.In this paper, we present a new classification method using magnetic induction spectroscopy (MIS) to sort three high-value metals that make up the majority of the non-ferrous fractioncopper, aluminium and brass. Two approaches are investigated: The first uses MIS with a set of geometric features returned by a vision system, where metal fragments are matched to known test pieces from a training set. The second approach uses MIS only. A surprisingly effective classifier can be constructed by combining the MIS frequency components in a manner determined by how eddy currents circulate in the metal fragment. An average precision and recall (purity and recovery rate) of around 92% was shown. This has significant industrial relevance, as the MIS-only classifier is simple, scalable, and straightforward to implement on existing commercial sorting lines.

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Section: Classificationmentioning

confidence: 99%

Classification of Nonferrous Metals Using Magnetic Induction Spectroscopy

O’Toole

Karimian

Peyton

2018

IEEE Trans. Ind. Inf.

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“…In practice, the system configuration, e.g., hardware and software implementation, differs from instrument to instrument and highly depends on each particular application. In general, the system contains an excitation source, which can be pulsed excitation signal [75], single frequency signal [28,76,77] and multi-frequency signal [78][79][80][81][82], etc. The hardware, data acquisition system (DAQ), normally includes analogue and digital conditioning electronics, which implement signal generation, signal amplification, demodulation and filtering.…”

Section: Algorithmmentioning

confidence: 99%

Eddy Current Measurement for Planar Structures

Xia

Huang

Chen

et al. 2022

Sensors

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Eddy current (EC) testing has become one of the most common techniques for measuring metallic planar structures in various industrial scenarios such as infrastructures, automotive, manufacturing, and chemical engineering. There has been significant progress in measuring the geometry, electromagnetic properties, and defects of metallic planar structures based on electromagnetic principles. In this review, we summarize recent developments in EC computational models, systems, algorithms, and measurement approaches for planar structures. First, the computational models including analytical models, numerical methods, and plate property estimation algorithms are introduced. Subsequently, the impedance measurement system and probes are presented. In plate measurements, sensor signals are sensitive to probe lift-off, and various algorithms for reducing the lift-off effect are reviewed. These approaches can be used for measureing thickness and electromagnetic properties. Furthermore, defect detection for metallic plates is also discussed.

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“…This proved to be impractical in terms of the solve time required for the rate of material and that only partial 3D geometric information could ever be measured. However in more recent work [13], [14], we were able to demonstrate a surprisingly simpleyet-effective algorithm could be devised to separate copper, aluminium and brass fragments -metals which compose the majority of non-ferrous waste fraction [15], [16] -using only two frequency components of the impedance spectra. Purity and recovery-rates of ≈92% were found using test fragments manufactured in to random shapes cut from known control metal stock.…”

Section: Introductionmentioning

confidence: 99%

Classification of Non-ferrous Scrap Metal using Two Component Magnetic Induction Spectroscopy

OrToole

Peyton

2019

2019 IEEE Sensors Applications Symposium (SAS)

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Magnetic induction spectroscopy is the measurement of how a conductive object reflects and scatters a magnetic field over different frequencies in response to some excitation magnetic field. In recent work, we proposed using this technique to classify different non-ferrous metals for the recycling and resource recovery sector-specifically, to identify fragments of scrap aluminium, copper and brass in shredded waste streams for separation and recovery. We proposed a simple algorithm that used only two components of the spectra that gave strong purity and recovery-rates when tested on a manufactured control set cut from stock metals. In this paper, we reexamined this method using real scrap metal samples drawn from a commercial sorting line. We found moderate purity and recovery-rates of brass and copper of between around 70% and 90%. However, the classification of aluminium was poor with ≈55% and ≈80% purity and recovery rates respectively. Magnetic induction sensors are a natural fit for the specifications of the industry. They are capable of highthroughputs, are unaffected by dirt or contaminants and are mechanically and physically robust. Although our results are modest, they are not insignificant given the simplicity of the algorithm and the relatively low-cost of instrumentation. Our work suggests the MIS as a technique may have a significant role to play in the extraction and recovery of non-ferrous resources.

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Fast classification of non-magnetic metal targets using eddy-current based impedance spectroscopy

Cited by 6 publications

References 6 publications

Classification of Nonferrous Metals Using Magnetic Induction Spectroscopy

Classification of Nonferrous Metals Using Magnetic Induction Spectroscopy

Eddy Current Measurement for Planar Structures

Classification of Non-ferrous Scrap Metal using Two Component Magnetic Induction Spectroscopy

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