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 (5/5 displayed)

  • 2023Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning24citations
  • 2022An Investigation on Effects by Adding Cerium and Lithium on Mechanical and Metallurgical Properties of AZ91D + XCe + XLi Cast Mg Alloy4citations
  • 2022Investigation on Mechanical Properties of Bamboo and Coconut Fiber with Epoxy Hybrid Polymer Composite15citations
  • 2021Investigation on Friction Stir Weldability Characteristics of AA7075-T651 and AA6061-T6 Based Nanocomposites7citations
  • 2021Investigation on Friction Stir Weldability Characteristics of AA7075-T651 and AA6061-T6 Based Nanocomposites7citations

Places of action

Chart of shared publication
Sujatha, P.
1 / 2 shared
Selvaraju, P.
1 / 4 shared
Kaarthik, M.
1 / 1 shared
Sathish, K.
1 / 1 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
Das, A. Daniel
1 / 4 shared
Manivannan, S.
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Kannan, C. Ramesh
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Krishnan, R.
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Raman, Velpuri Venkat
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Bhattacharya, Sumanta
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Vezhavendhan, R.
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Prabhu, S. Venkatesa
1 / 8 shared
Bommanna, K.
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Sunagar, Prashant
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Manjunath, Dr. Madhusudan
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Krishnamurthy, R.
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John, Dr. Jacob
1 / 2 shared
Kurse, Shridhar
1 / 1 shared
Shanmuganatan, Subramanian Pillappan
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John, Jacob
1 / 1 shared
Madhusudan, M.
1 / 3 shared
Shanmuganatan, S. P.
1 / 3 shared
Shridhar, Kurse
1 / 1 shared
Chart of publication period
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2022
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Co-Authors (by relevance)

  • Sujatha, P.
  • Selvaraju, P.
  • Kaarthik, M.
  • Sathish, K.
  • Prasanth, I. S. N. V. R.
  • Ahammad, S. K. Hasane
  • Mayakannan, S.
  • Jeevanandam, Prabahar
  • Das, A. Daniel
  • Manivannan, S.
  • Kannan, C. Ramesh
  • Krishnan, R.
  • Kumar, P. Sathish
  • Raman, Velpuri Venkat
  • Bhattacharya, Sumanta
  • Vezhavendhan, R.
  • Prabhu, S. Venkatesa
  • Bommanna, K.
  • Sunagar, Prashant
  • Manjunath, Dr. Madhusudan
  • Krishnamurthy, R.
  • John, Dr. Jacob
  • Kurse, Shridhar
  • Shanmuganatan, Subramanian Pillappan
  • John, Jacob
  • Madhusudan, M.
  • Shanmuganatan, S. P.
  • Shridhar, Kurse
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