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

Madasu, Vamsi

  • Google
  • 1
  • 2
  • 0

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2008Fuzzy modeling based estimation of short circuit severity in pulse gas metal arc weldingcitations

Places of action

Chart of shared publication
Posinaseeti, Praveen
1 / 3 shared
Yarlagadda, Prasad Kdv
1 / 50 shared
Chart of publication period
2008

Co-Authors (by relevance)

  • Posinaseeti, Praveen
  • Yarlagadda, Prasad Kdv
OrganizationsLocationPeople

document

Fuzzy modeling based estimation of short circuit severity in pulse gas metal arc welding

  • Posinaseeti, Praveen
  • Yarlagadda, Prasad Kdv
  • Madasu, Vamsi
Abstract

Avoiding short circuit is an essential condition for achieving good quality welds in Pulse GasMetal Arc Welding (GMAW-P). Estimating short circuit in any welding process is dependent onproper selection and optimization of welding process parameters. Such optimization is critical inthe GMAW-P wherein wire melting is closely dictated by numerous pulsing parameters incomparison to the conventional GMAW process. Fuzzy Logic based models are an excellentalternative in such situations where a complex relationship between the large number ofpredictor variables (independents, inputs) and predicted variables (dependents, outputs) existand are not easy to articulate in the usual terms of correlations or differences between groups. Inthis paper, we have proposed an input output fuzzy model for estimating the short circuitseverity in terms of number of shorts per pulse for GMAW-P process. Eighteen factorsrepresenting the characteristics of the pulse waveforms are employed as predictor variables andthe short circuit severity (or number of shorts per pulse) is predicted on the basis of a modifiedexponential membership function fitted to the fuzzy sets derived from predictor variables. Theexponential membership function is modified by two structural parameters that are estimatedby optimizing the criterion function associated with the fuzzy modeling. The experimental dataconsists of GMAW-P welding of 6XXX group of aluminum alloys. The results demonstrate thatproposed fuzzy model could estimate the short circuit severity with high accuracy.

Topics
  • impedance spectroscopy
  • aluminium
  • wire