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

Chitwadgi, Riddhisha

  • Google
  • 1
  • 4
  • 1

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2022Optimization and analysis of dry sliding wear behaviour of N-B4C/MOS2 unreinforced AA2219 nano hybrid composites using response surface methodology1citations

Places of action

Chart of shared publication
Siddesh, B.
1 / 1 shared
Shankar, B. Latha
1 / 1 shared
Suresh, R.
1 / 5 shared
Siddeshkumar, N. G.
1 / 1 shared
Chart of publication period
2022

Co-Authors (by relevance)

  • Siddesh, B.
  • Shankar, B. Latha
  • Suresh, R.
  • Siddeshkumar, N. G.
OrganizationsLocationPeople

article

Optimization and analysis of dry sliding wear behaviour of N-B4C/MOS2 unreinforced AA2219 nano hybrid composites using response surface methodology

  • Siddesh, B.
  • Chitwadgi, Riddhisha
  • Shankar, B. Latha
  • Suresh, R.
  • Siddeshkumar, N. G.
Abstract

<jats:p>The effect of heat treatment on nano-size B4C particle reinforced hybrid composites is discussed in this paper. For this, hybrid reinforced AA2219 composites with 2% by weight nano B4C and 2% by weight MoS2 particulates were fabricated using a two-stage stir casting process, and the specimens were heat treated to assess their influence on wear behavior. Experiments were carried out to study the wear behavior by varying important factors such as aging temperature, load, and sliding distance. Response Surface Methodology (RSM) designed by Box-Behnken was used to identify the critical variables influencing wear rate and optimize wear behavior. To comprehend the wear mechanisms involved, an analysis of the worn surface was presented. Based on the analysis, a regression equation with a predictability of 97.2% was developed for the response to obtain the optimum wear rate. The following order effectively captures the relative importance of the various factors determining the alloy's wear resistance: sliding distance, load, and aging temperature. When compared to load and sliding distance, heat treatments via artificial aging in the temperature range of 200-240 °C have no significant effect on the wear resistance of hybrid AA2219 composites reinforced with n-B4C and MoS2 particulates. However, when a temperature range of 200-240 °C is considered, composites exhibit better wear resistance at the aging temperature of 240 °C with ice quenching.</jats:p>

Topics
  • impedance spectroscopy
  • surface
  • experiment
  • wear resistance
  • composite
  • casting
  • aging
  • aging
  • quenching