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

Tosi, R.

  • Google
  • 1
  • 4
  • 112

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2014On optimization of surface roughness of selective laser melted stainless steel parts: A statistical study112citations

Places of action

Chart of shared publication
Manning, Warren
1 / 2 shared
Moroz, A.
1 / 1 shared
Wimpenny, D.
1 / 2 shared
Alrbaey, K.
1 / 2 shared
Chart of publication period
2014

Co-Authors (by relevance)

  • Manning, Warren
  • Moroz, A.
  • Wimpenny, D.
  • Alrbaey, K.
OrganizationsLocationPeople

article

On optimization of surface roughness of selective laser melted stainless steel parts: A statistical study

  • Manning, Warren
  • Moroz, A.
  • Wimpenny, D.
  • Alrbaey, K.
  • Tosi, R.
Abstract

In this work, the effects of re-melting parameters for postprocessing the surface texture of Additively Manufactured parts using a statistical approach are investigated. This paper focuses on improving the final surface texture of stainless steel (316L) parts, built using a Renishaw SLM 125 machine. This machine employs a fiber laser to fuse fine powder on a layer-by-layer basis to generate three-dimensional parts. The samples were produced using varying angles of inclination in order to generate range of surface roughness between 8 and 20 µm. Laser re-melting (LR) as post-processing was performed in order to investigate surface roughness through optimization of parameters. The re-melting process was carried out using a custom-made hybrid laser re-cladding machine, which uses a 200 W fiber laser. Optimized processing parameters were based on statistical analysis within a Design of Experiment framework, from which a model was then constructed. The results indicate that the best obtainable final surface roughness is about 1.4 µm ± 10%. This figure was obtained when laser power of about 180 W was used, to give energy density between 2200 and 2700 J/cm2 for the re-melting process. Overall, the obtained results indicate LR as a post-build process has the capacity to improve surface finishing of SLM components up to 80%, compared with the initial manufactured surface.

Topics
  • density
  • impedance spectroscopy
  • surface
  • energy density
  • stainless steel
  • experiment
  • texture