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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Materials Map under construction

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 (2/2 displayed)

  • 2023Taguchi optimization and modelling of stir casting process parameters on the percentage elongation of aluminium, pumice and carbonated coal composite15citations
  • 2022Mechanical properties and corrosion behaviour of duplex stainless steel weldment using novel electrodes16citations

Places of action

Chart of shared publication
Ibrahim, Tanimu Kogi
1 / 1 shared
Adebisi, Adetayo Abdulmumin
1 / 1 shared
Dan-Asabe, Bashar
1 / 1 shared
Omiogbemi, Ibrahim Momoh-Bello
1 / 1 shared
Das, Atanu
1 / 2 shared
Gorja, Sudhakar Rao
1 / 1 shared
Chowdhury, Sandip Ghosh
1 / 2 shared
Dauda, Emmanuel Toi
1 / 1 shared
Kumar, Roshan
1 / 1 shared
Afolayan, Matthew Olatunde
1 / 1 shared
Chart of publication period
2023
2022

Co-Authors (by relevance)

  • Ibrahim, Tanimu Kogi
  • Adebisi, Adetayo Abdulmumin
  • Dan-Asabe, Bashar
  • Omiogbemi, Ibrahim Momoh-Bello
  • Das, Atanu
  • Gorja, Sudhakar Rao
  • Chowdhury, Sandip Ghosh
  • Dauda, Emmanuel Toi
  • Kumar, Roshan
  • Afolayan, Matthew Olatunde
OrganizationsLocationPeople

article

Taguchi optimization and modelling of stir casting process parameters on the percentage elongation of aluminium, pumice and carbonated coal composite

  • Ibrahim, Tanimu Kogi
  • Adebisi, Adetayo Abdulmumin
  • Dan-Asabe, Bashar
  • Yawas, Danjuma Saleh
Abstract

<jats:title>Abstract</jats:title><jats:p>Aluminium matrix composites, which are a subclass of metal matrix composites, have characteristics including low density, high stiffness and strength, better wear resistance, controlled thermal expansion, greater fatigue resistance, and improved stability at high temperatures. The scientific and industrial communities are interested in these composites because they may be used to manufacture a broad variety of components for cutting-edge applications. This has study observed how the stirring speed, processing temperature, and stirring duration of the stir casting process affected the percentage elongation of Al-Pumice (PP)-Carbonized Coal Particles (CCP) hybrid composites. It also looked at the optimal weight of these natural ceramic reinforcements using the Taguchi optimization technique. While optimizing the percentage elongation property, the hard compound such as silica, iron oxide, and alumina, were discovered during the characterisation of the reinforcement, showing that PP and CCP can be used as reinforcement in metal matrix composite. The percentage of elongation of the hybrid composite was shown to be most affected by the PP, followed by processing temperature, stirring speed, CCP, and stirring time, using stir casting process parameter optimization. It was observed at 2.5 wt% of pumice particles, 2.5 wt% of carbonated coal particles, 700 °C processing temperature, 200 rpm stirring speed, and 5 min stirring time, the optimum percentage of elongation was discovered to be 5.6%, which is 25.43% lower than the percentage elongation of Al-alloy without reinforcing. The regression study developed a predictive mathematical model for the percentage elongation (PE) as a function of the stir casting process parameters and offered a high degree of prediction, with R-Square, R-Square (adj), and R-Square (pred) values of 91.60%, 87.41%, and 79.32% respectively.</jats:p>

Topics
  • density
  • impedance spectroscopy
  • compound
  • aluminium
  • wear resistance
  • strength
  • fatigue
  • composite
  • thermal expansion
  • casting
  • iron
  • ceramic