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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Ozoegwu, Chigbogu G.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (8/8 displayed)

  • 2022Mechanical properties, tribology and electrochemical studies of Al/Fly ash/eggshell aluminium matrix composite18citations
  • 2022The influence of sustainable reinforcing particulates on the density, hardness and corrosion resistance of AA 6063 matrix composites13citations
  • 2022Evaluation of particle size distribution, mechanical properties, microstructure and electrochemical studies of AA1050/fly ash metal matrix composite9citations
  • 2022Effects of carbonised eggshells on the mechanical properties, microstructure and corrosion resistance of AA1050 of metal matrix composites18citations
  • 2021Characterization, machinability studies, and multi-response optimization of AA 6082 hybrid metal matrix composite15citations
  • 2021Carbonization temperature and its effect on the mechanical properties, wear and corrosion resistance of aluminum reinforced with eggshell11citations
  • 2021Machinability studies and optimization of aa 6082/fly ash/carbonized eggshell matrix composite6citations
  • 2020A concise review of the effects of hybrid particulate reinforced aluminium metal matrix composites on the microstructure, density and mechanical properties7citations

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Chart of shared publication
Madushele, Nkosinathi
6 / 15 shared
Akinlabi, Esther Titilayo
8 / 235 shared
Akinribide, Ojo Jeremiah
1 / 9 shared
Ononiwu, Ndudim H.
6 / 6 shared
Jacobs, Ifeanyi Okoro
1 / 1 shared
Nwachukwu, Victor C.
1 / 1 shared
Ononiwu, Ndudim Henry
1 / 1 shared
Ononiwu, Ndudim
1 / 1 shared
Aigbodion, Victor
1 / 1 shared
Chart of publication period
2022
2021
2020

Co-Authors (by relevance)

  • Madushele, Nkosinathi
  • Akinlabi, Esther Titilayo
  • Akinribide, Ojo Jeremiah
  • Ononiwu, Ndudim H.
  • Jacobs, Ifeanyi Okoro
  • Nwachukwu, Victor C.
  • Ononiwu, Ndudim Henry
  • Ononiwu, Ndudim
  • Aigbodion, Victor
OrganizationsLocationPeople

article

Characterization, machinability studies, and multi-response optimization of AA 6082 hybrid metal matrix composite

  • Madushele, Nkosinathi
  • Akinlabi, Esther Titilayo
  • Ozoegwu, Chigbogu G.
  • Ononiwu, Ndudim H.
Abstract

<p>This work investigated the effect of carbonized eggshell and fly ash on the microstructure, mechanical properties, and machinability of AA 6082. The fabrication method selected for this study was stir casting. For the hybrid metal matrix composite, the weight fraction was 2.5wt% carbonized eggshell and 2.5wt% fly ash. Density analysis recorded a 10.66% reduction of the cast composite in comparison to the aluminum alloy. Improvements of 12.32%, 21.91%, and 8.30% were recorded for the microhardness, tensile strength, and compressive strength respectively. The wear studies of the cast samples revealed coefficients of friction (CoF) of 0.499 and 0.290 for the base metal and the composite respectively. For the machinability studies, the surface roughness and tool flank wear were the responses under consideration. The design of experiments was conducted using the Taguchi L<sub>16</sub> orthogonal array. The input parameters for this investigation were cutting speeds (100 mm/min, 200 mm/min, 300 mm/min, 400 mm/min), feeds (0.1 mm/rev, 0.2 mm/rev, 0.3 mm/rev, 0.4 mm/rev), and depths of cut (0.25 mm, 0.50 mm, 0.75 mm, 1.0 mm). For the multi-response optimization, Taguchi-based grey relational analysis was used. The analysis of variance (ANOVA) of the grey relational grade (GRG) revealed that the feed was the most influential factor on the GRG. The initial optimization showed the optimal cutting speed, feed, and depth of cut as 100 mm/min, 0.1 mm/rev, and 0.25 mm respectively. The confirmatory tests revealed that the optimal combination of factors was 400mm/min, 0.1 mm/rev, and 0.25 mm for the cutting speed, feed, and depth of cut respectively.</p>

Topics
  • density
  • microstructure
  • surface
  • experiment
  • aluminium
  • strength
  • composite
  • casting
  • tensile strength