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 (4/4 displayed)

  • 2023Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning24citations
  • 2021Thermal investigation of a solar box-type cooker with nanocomposite phase change materials using flexible thermography69citations
  • 2021Thermal investigation of a solar box-type cooker with nanocomposite phase change materials using flexible thermography69citations
  • 2018Exploration of Device in Mechanical Properties - Treated Coir Sheath by Epoxy Resin expenditure Fuzzy Logic Rule1citations

Places of action

Chart of shared publication
Sujatha, P.
1 / 2 shared
Kaarthik, M.
1 / 1 shared
Sathish, K.
1 / 1 shared
Sasikumar, Bashyam
1 / 5 shared
Prasanth, I. S. N. V. R.
1 / 2 shared
Ahammad, S. K. Hasane
1 / 2 shared
Mayakannan, S.
1 / 4 shared
Jeevanandam, Prabahar
1 / 1 shared
Chithambaram, V.
2 / 2 shared
Janarthanan, B.
2 / 2 shared
A., Essa F.
1 / 2 shared
I., Pruncu C.
1 / 1 shared
H., Elsheikh A.
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Shanmugan, S.
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Palanikumar, G.
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E., Kabeel A.
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Gorjian, Shiva
1 / 1 shared
Panchal, Hitesh
1 / 2 shared
Pruncu, Catalin I.
1 / 28 shared
Ebadi, Hossein
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Elsheikh, Ammar H.
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Kabeel, A. E.
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Essa, F. A.
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Monika Shree, V.
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Sangeetha, M.
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Keerthana, V.
1 / 1 shared
Sai Malar, S.
1 / 1 shared
Palani, S.
1 / 1 shared
Chart of publication period
2023
2021
2018

Co-Authors (by relevance)

  • Sujatha, P.
  • Kaarthik, M.
  • Sathish, K.
  • Sasikumar, Bashyam
  • Prasanth, I. S. N. V. R.
  • Ahammad, S. K. Hasane
  • Mayakannan, S.
  • Jeevanandam, Prabahar
  • Chithambaram, V.
  • Janarthanan, B.
  • A., Essa F.
  • I., Pruncu C.
  • H., Elsheikh A.
  • Shanmugan, S.
  • Gorjian, S.
  • Panchal, H.
  • Ebadi, H.
  • Palanikumar, G.
  • E., Kabeel A.
  • Gorjian, Shiva
  • Panchal, Hitesh
  • Pruncu, Catalin I.
  • Ebadi, Hossein
  • Elsheikh, Ammar H.
  • Kabeel, A. E.
  • Essa, F. A.
  • Monika Shree, V.
  • Sangeetha, M.
  • Keerthana, V.
  • Sai Malar, S.
  • Palani, S.
OrganizationsLocationPeople

article

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

  • Sujatha, P.
  • Selvaraju, P.
  • Kaarthik, M.
  • Sathish, K.
  • Sasikumar, Bashyam
  • Prasanth, I. S. N. V. R.
  • Ahammad, S. K. Hasane
  • Mayakannan, S.
  • Jeevanandam, Prabahar
Abstract

<jats:p>In this research, the friction and wear of AA7075 nanocomposites reinforced with graphene and graphite were studied. Graphene’s inclusion dramatically enhanced the material’s mechanical characteristics, friction, and wear resistance. AA7075 is strengthened with less graphene, and AA7075, reinforced with more graphite, exhibits similar wear and friction behavior. Wear rate and coefficient of friction predictions for AA7075-graphene nanocomposites were made using five machine learning (ML) regression models. ML simulations reveal that the wear and friction of AA7075-graphene composites are most sensitive to the proportion of graphene presence, the loadings, and the hardness.</jats:p>

Topics
  • nanocomposite
  • impedance spectroscopy
  • inclusion
  • simulation
  • laser emission spectroscopy
  • wear resistance
  • hardness
  • machine learning
  • coefficient of friction