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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Esakkiraj, E. S.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (5/5 displayed)

  • 2023Evaluation of Mechanical Behaviour of Multiwalled Nanotubes Reinforcement Particles in Jute-Glass Fibres Hybrid Composites3citations
  • 2023Optimization of Filler Content and Size on Mechanical Performance of Graphene/Hemp/Epoxy-Based Hybrid Composites using Taguchi with ANN Technique39citations
  • 2023Optimization of Filler Content and Size on Mechanical Performance of Graphene/Hemp/Epoxy-Based Hybrid Composites using Taguchi with ANN Technique39citations
  • 2023Tribology analysis of MMT nanoclay alkali-treated coconut sheath reinforced hybrid compositecitations
  • 2022Investigation of Physicochemical Properties and Characterization of Leaf Stalk Fibres Extracted from the Caribbean Royal Palm Tree47citations

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Kaliappan, S.
3 / 24 shared
Mothilal, T.
1 / 1 shared
Bharathi, B. Raja
1 / 1 shared
Pravin, P.
1 / 1 shared
Dineshkumar, M.
2 / 3 shared
Bhaskar, A.
2 / 4 shared
Patil, Pravin P.
2 / 30 shared
Kaliappan, Dr. S.
1 / 1 shared
Natrayan, L.
2 / 27 shared
Selvam, M.
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Rajaravi, C.
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Srinivasan, R. Ganapathy
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Nagarajan, Pragadish
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Gurupranes, S. V.
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Patel, Praveen Bhai
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Sekar, S.
1 / 15 shared
Jayaraman, P.
1 / 3 shared
Pandian, C. K. Arvinda
1 / 1 shared
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2022

Co-Authors (by relevance)

  • Kaliappan, S.
  • Mothilal, T.
  • Bharathi, B. Raja
  • Pravin, P.
  • Dineshkumar, M.
  • Bhaskar, A.
  • Patil, Pravin P.
  • Kaliappan, Dr. S.
  • Natrayan, L.
  • Selvam, M.
  • Rajaravi, C.
  • Srinivasan, R. Ganapathy
  • Nagarajan, Pragadish
  • Gurupranes, S. V.
  • Patel, Praveen Bhai
  • Sekar, S.
  • Jayaraman, P.
  • Pandian, C. K. Arvinda
OrganizationsLocationPeople

article

Optimization of Filler Content and Size on Mechanical Performance of Graphene/Hemp/Epoxy-Based Hybrid Composites using Taguchi with ANN Technique

  • Esakkiraj, E. S.
  • Dineshkumar, M.
  • Bhaskar, A.
  • Patil, Pravin P.
  • Kaliappan, Dr. S.
Abstract

<jats:p>The usage of nanofillers in composite materials has grown over time due to various benefits, including superior properties, better adhesion, and high stiffness. To accomplish this, 150, 200, 250, and 300 gsm of hemp fiber mat with various thicknesses and weight proportions of graphene powder, including 0%, 3%, 6%, and 9%, as well as 3, 6, 18, and 25 µm-sized particles, were used. High-speed mechanical stirring was used to evenly mix the nanofiller (nanographene) with the epoxy-based nanocomposites at various loadings. We looked at the bending and interlaminar shear strength (ILSS) properties of hybrid nanomaterials. According to the study, adding 300 gsm of hemp epoxy composites filled with 6 wt% nanographene has significantly improved mechanical properties. The development of a forecasting model to determine the mechanical properties using artificial neural networks (ANN). The constructed model has a significant connection with the test findings. A correlation of 0.9724 for the Levenberg–Marquardt training procedure indicates a significant connection between the predicted and experimental artificial neural models. The observational and projected results for bending and ILSS have &lt;3% and 4% errors, corresponding to the ANN prediction and Taguchi L16 matrix. The potential of ANN for forecasting the bending and ILSS of composite materials is expanded by the close relationship between ANN and experimental findings. The following parameters were used in the current study to determine the flexural strength: graphene content (40.79%), graphene size (34.19%), the number of hemp layers (12.57%), and hemp fiber thickness (11.65%). Similar to ILSS, graphene content accounts for 47.82% of the total, with graphene size (27.87%), hemp fiber thickness (11.80%), and the number of hemp layers (also 11.80%) all contributing (11.78%).</jats:p>

Topics
  • nanocomposite
  • impedance spectroscopy
  • strength
  • flexural strength