Materials Map

Discover the materials research landscape. Find experts, partners, networks.

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

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.

×

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.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Thirumalai Kumaran, S.

  • Google
  • 3
  • 10
  • 29

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2022Modelling Approach for the Prediction of Machinability in Al6061 Composites by Electrical Discharge Machining15citations
  • 2021MEASUREMENT OF MICROCHANNELS PRODUCED IN AA6351/RUTILE COMPOSITE BY WIRE-EDM14citations
  • 2019Evaluation of Electrical Discharge Machining Performance on Al (6351)–SiC–B4C Compositecitations

Places of action

Chart of shared publication
Uthayakumar, Marimuthu
1 / 1 shared
Kumar, Shanmugam Suresh
1 / 1 shared
Korniejenko, Kinga
1 / 10 shared
Ram, Hariharan Sree
1 / 1 shared
Uthayakumar, M.
2 / 10 shared
Kumar, S. Suresh
1 / 9 shared
Sreeraj, P.
1 / 2 shared
Pethuraj, M.
1 / 1 shared
Suresh Kumar, S.
1 / 1 shared
Parameswaran, P.
1 / 3 shared
Chart of publication period
2022
2021
2019

Co-Authors (by relevance)

  • Uthayakumar, Marimuthu
  • Kumar, Shanmugam Suresh
  • Korniejenko, Kinga
  • Ram, Hariharan Sree
  • Uthayakumar, M.
  • Kumar, S. Suresh
  • Sreeraj, P.
  • Pethuraj, M.
  • Suresh Kumar, S.
  • Parameswaran, P.
OrganizationsLocationPeople

article

Modelling Approach for the Prediction of Machinability in Al6061 Composites by Electrical Discharge Machining

  • Uthayakumar, Marimuthu
  • Kumar, Shanmugam Suresh
  • Korniejenko, Kinga
  • Thirumalai Kumaran, S.
  • Ram, Hariharan Sree
Abstract

<jats:p>This work aims to identify the pattern for the major output parameters, material removal rate (MRR) and surface roughness (Ra) of different combinations of Al6061-based composites. Based on the verification carried out on these patterns using analysis of variance (ANOVA) as the mathematical tool, the work predicts the mentioned output characteristics while machining Al6061 composites of different material compositions based on their hardness values. ANOVA was employed for the generation of equations of the particular composite. The equations were compared for the coefficients of each parameter employed in ANOVA. The work was carried out comparing the characteristic equation of different combinations of Al6061-based composite. The results indicate that the coefficients of the current show a drastic variation when compared to other coefficients for both the output parameters. It was observed that the current and its coefficients contribute to the output parameters based on the variation in hardness. For surface roughness, the constant of the characteristic equation was also found to influence the parameter for the change in hardness. The equation derived for both material removal rate (MRR) and surface roughness (Ra) were identified to be matching with the experimental result carried out for validation. The average variation observed was 9.3% for MRR and 7.2% for surface roughness.</jats:p>

Topics
  • surface
  • composite
  • hardness