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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Otoole, Michael D.
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Publications (8/8 displayed)
- 2024Classification of Wrought and Cast Aluminium using Magnetic Induction Spectroscopy and Machine Vision
- 2023A review of the classification of non-ferrous metals using magnetic induction for recyclingcitations
- 2023Scrap metal classification using magnetic induction spectroscopy and machine visioncitations
- 2019Classification of Non-ferrous Scrap Metal using Two Component Magnetic Induction Spectroscopy
- 2017Classification of Non-ferrous Metals using Magnetic Induction Spectroscopycitations
- 2017Electromagnetic tensor spectroscopy for sorting of shredded metallic scrapcitations
- 2017Selective recovery of metallic scraps using electromagnetic tensor spectroscopy
- 2015Rapid Non-Contact Relative Permittivity Measurement of Fruits and Vegetables using Magnetic Induction Spectroscopycitations
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document
Classification of Wrought and Cast Aluminium using Magnetic Induction Spectroscopy and Machine Vision
Abstract
Recycled aluminium can reduce the greenhouse gas emissions created and energy required to produce aluminium compared to virgin bauxite ore. Once aluminium is separated from other non-ferrous metals, it is labelled ‘Twitch’ and consists of wrought and cast aluminium. Wrought is removed to avoid contamination from the cast pieces, as contamination undermines the alloy’s sustainability and changes the metals’ properties. In this paper, we demonstrate the use of magnetic induction spectroscopy to classify wrought from cast independently and combined with a machine vision camera on a conveyor. The magnetic induction sensor measures 6 frequencies between a range of 2736 to 59508 Hz. The camera extracts the colour, perimeter, area and offset of the metal piece. The combinations of induction, induction and shape, induction and colour, and colour are tried to determine the best sensor combination. We first show how wrought can be classified with induction only with a 71.21-85.58% recovery and 74.6-83.26% purity. We then show how the combination of induction and the colour of the metal pieces as features can increase the recovery to 71.21-92.56% and the purity to 83.92-88.05%. Classification using colour only obtained an F1 score of 0.598-0.789, whereas induction only had an F1 score of 0.844-0.729. The addition of shape as a feature did not noticeably improve the recovery and purity.