Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (8/8 displayed)

  • 2024Classification of Wrought and Cast Aluminium using Magnetic Induction Spectroscopy and Machine Visioncitations
  • 2023A review of the classification of non-ferrous metals using magnetic induction for recycling3citations
  • 2023Scrap metal classification using magnetic induction spectroscopy and machine vision14citations
  • 2019Classification of Non-ferrous Scrap Metal using Two Component Magnetic Induction Spectroscopycitations
  • 2017Classification of Non-ferrous Metals using Magnetic Induction Spectroscopy51citations
  • 2017Electromagnetic tensor spectroscopy for sorting of shredded metallic scrap5citations
  • 2017Selective recovery of metallic scraps using electromagnetic tensor spectroscopycitations
  • 2015Rapid Non-Contact Relative Permittivity Measurement of Fruits and Vegetables using Magnetic Induction Spectroscopy6citations

Places of action

Chart of shared publication
Williams, Kane C.
3 / 3 shared
Peyton, Antony J.
8 / 19 shared
Mallaburn, Michael
1 / 1 shared
Karimian, Noushin
3 / 8 shared
Davidson, J. L.
1 / 1 shared
Marsh, Liam
1 / 1 shared
Armitage, David
1 / 3 shared
Tan, Y. M.
1 / 1 shared
Chart of publication period
2024
2023
2019
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Co-Authors (by relevance)

  • Williams, Kane C.
  • Peyton, Antony J.
  • Mallaburn, Michael
  • Karimian, Noushin
  • Davidson, J. L.
  • Marsh, Liam
  • Armitage, David
  • Tan, Y. M.
OrganizationsLocationPeople

document

Classification of Wrought and Cast Aluminium using Magnetic Induction Spectroscopy and Machine Vision

  • Otoole, Michael D.
  • Williams, Kane C.
  • Peyton, Antony J.
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.

Topics
  • impedance spectroscopy
  • aluminium