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

Rashvand, Kaveh

  • Google
  • 3
  • 8
  • 17

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2024Fabric compaction and fibre volume fraction evaluation for vacuum-assisted resin infusion modellingcitations
  • 2024In-situ and adhesive repair of continuous fiber composites using 3D printing17citations
  • 2023Parametric and numerical Finite Element simulation of wind turbine blades subjected to thermal residual stressescitations

Places of action

Chart of shared publication
Pierce, Robert S.
1 / 12 shared
Haselbach, Philipp Ulrich
1 / 6 shared
Larionova, Anastasiia
1 / 1 shared
Sarhadi, Ali
1 / 12 shared
Eder, Martin Alexander
1 / 13 shared
Ayyobi, Pedram
1 / 3 shared
Ayoubi, Peyman
1 / 1 shared
Mohammadi, Moloud
1 / 1 shared
Chart of publication period
2024
2023

Co-Authors (by relevance)

  • Pierce, Robert S.
  • Haselbach, Philipp Ulrich
  • Larionova, Anastasiia
  • Sarhadi, Ali
  • Eder, Martin Alexander
  • Ayyobi, Pedram
  • Ayoubi, Peyman
  • Mohammadi, Moloud
OrganizationsLocationPeople

document

Fabric compaction and fibre volume fraction evaluation for vacuum-assisted resin infusion modelling

  • Pierce, Robert S.
  • Haselbach, Philipp Ulrich
  • Larionova, Anastasiia
  • Rashvand, Kaveh
Abstract

To consistently achieve high-quality and reliable composite laminates, reducing the defects introduced while manufacturing is crucial. One of the methods to help is obtaining an optimal infusion strategy that considers the local compressed state during the vacuum infusion process. To create a unique digital twin for infusion processing planning, both accurate and practical methods must be used to obtain the geometry of the preform. This study has considered blue-light scanning, digital image correlation and laser sensor measurements for non-destructive in-situ fibre volume fraction assessments. The obtained results have also been compared with post-mortem burn-off testing of specimens and subsequently evaluated for filling time calculations with PAM-RTM compared against the actual filling duration.

Topics
  • impedance spectroscopy
  • simulation
  • composite
  • defect
  • resin