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

Straka, Ľuboslav

  • Google
  • 3
  • 3
  • 71

Technical University of Košice

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2017Statistical Approach to Optimize the Process Parameters of HAZ of Tool Steel EN X32CrMoV12-28 after Die-Sinking EDM with SF-Cu Electrode25citations
  • 2017Prediction of the Geometrical Accuracy of the Machined Surface of the Tool Steel EN X30WCrV9-3 after Electrical Discharge Machining with CuZn37 Wire Electrode15citations
  • 2016Properties Evaluation of Thin Microhardened Surface Layer of Tool Steel after Wire EDM31citations

Places of action

Chart of shared publication
Hašová, Slavomíra
1 / 1 shared
Corny, Ivan
3 / 3 shared
Piteľ, Ján
3 / 5 shared
Chart of publication period
2017
2016

Co-Authors (by relevance)

  • Hašová, Slavomíra
  • Corny, Ivan
  • Piteľ, Ján
OrganizationsLocationPeople

article

Prediction of the Geometrical Accuracy of the Machined Surface of the Tool Steel EN X30WCrV9-3 after Electrical Discharge Machining with CuZn37 Wire Electrode

  • Straka, Ľuboslav
  • Corny, Ivan
  • Piteľ, Ján
Abstract

The geometrical accuracy of the machined surface can generally be understood mainly as accuracy of shape, orientation, position and run-out. As a general rule; it is quantified by the corresponding deviations from the nominal area. The size of the geometric deviation from the nominal area may in practice affect the conventionally measured value of the dimension, even if the required dimensional tolerance is adhered to. Since electro–erosive machining technology belongs to very precise finishing technologies; even the small geometrical accuracy deviation has a negative impact on the resulting quality of machined surfaces. The aim of the experiments was to contribute to the knowledge database, which defines the influence of the process parameters at electrical discharge machining with the CuZn37 tool electrode on errors of geometrical accuracy of the machined surface. On the basis of the results of the experimental measurements, graphical dependencies were determined which predict geometrical accuracy of the machined surface in terms of the maximum deviation of flatness after electrical discharge machining of tool steel EN X30WCrV9-3 (W.-Nr. 1.2581) with CuZn37 wire electrode of 0.20 mm diameter to determine the appropriate combination of process parameters.

Topics
  • impedance spectroscopy
  • surface
  • experiment
  • tool steel
  • wire