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

Broer, Agnes A. R.

  • Google
  • 11
  • 12
  • 172

Delft University of Technology

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (11/11 displayed)

  • 2023Intelligent Health Indicators Based on Semi-supervised Learning Utilizing Acoustic Emission Data3citations
  • 2023Hierarchical Upscaling of Data-Driven Damage Diagnostics for Stiffened Composite Aircraft Structurescitations
  • 2023Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data45citations
  • 2023An SHM Data-Driven Methodology for the Remaining Useful Life Prognosis of Aeronautical Subcomponents6citations
  • 2023A novel strain-based health indicator for the remaining useful life estimation of degrading composite structures11citations
  • 2022On the Challenges of Upscaling Damage Monitoring Methodologies for Stiffened Composite Aircraft Panels2citations
  • 2022Assessing stiffness degradation of stiffened composite panels in post-buckling compression-compression fatigue using guided waves24citations
  • 2021A Strain-Based Health Indicator for the SHM of Skin-to-Stringer Disbond Growth of Composite Stiffened Panels in Fatigue10citations
  • 2021Health monitoring of aerospace structures utilizing novel health indicators extracted from complex strain and acoustic emission data26citations
  • 2021Fusion-based damage diagnostics for stiffened composite panels37citations
  • 2021Health indicators for diagnostics and prognostics of composite aerospace structures8citations

Places of action

Chart of shared publication
Benedictus, Rinze
5 / 27 shared
Zarouchas, Dimitrios
11 / 30 shared
Moradi, Morteza
2 / 11 shared
Chiachío, Juan
2 / 7 shared
Loutas, Theodoros
9 / 13 shared
Galanopoulos, Georgios
8 / 10 shared
Yue, Nan
3 / 3 shared
Loutas, Theodoros H.
1 / 2 shared
Milanoski, Dimitrios
5 / 6 shared
Eleftheroglou, Nick
2 / 2 shared
Briand, William
1 / 2 shared
Rébillat, Marc
1 / 13 shared
Chart of publication period
2023
2022
2021

Co-Authors (by relevance)

  • Benedictus, Rinze
  • Zarouchas, Dimitrios
  • Moradi, Morteza
  • Chiachío, Juan
  • Loutas, Theodoros
  • Galanopoulos, Georgios
  • Yue, Nan
  • Loutas, Theodoros H.
  • Milanoski, Dimitrios
  • Eleftheroglou, Nick
  • Briand, William
  • Rébillat, Marc
OrganizationsLocationPeople

article

Fusion-based damage diagnostics for stiffened composite panels

  • Benedictus, Rinze
  • Zarouchas, Dimitrios
  • Loutas, Theodoros
  • Galanopoulos, Georgios
  • Broer, Agnes A. R.
Abstract

Conducting damage diagnostics on stiffened panels is commonly performed using a single SHM technique. However, each SHM technique has both its strengths and limitations. Rather than straining the expansion of single SHM techniques going beyond their intrinsic capacities, these strengths and limitations should instead be considered in their application. In this work, we propose a novel fusion-based methodology between data from two SHM techniques in order to surpass the capabilities of a single SHM technique. The aim is to show that by considering data fusion, a synergy can be obtained, resulting in a comprehensive damage assessment, not possible using a single SHM technique. For this purpose, three single-stiffener carbon–epoxy panels were subjected to fatigue compression after impact tests. Two SHM techniques monitored damage growth under the applied fatigue loads: acoustic emission and distributed fiber optic strain sensing. Four acoustic emission sensors were placed on each panel, thereby allowing for damage detection, localization, type identification (delamination), and severity assessment. The optical fibers were adhered to the stiffener feet’ surface, and its strain measurements were used for damage detection, disbond localization, damage type identification (stiffness degradation and disbond growth), and severity assessment. Different fusion techniques are presented in order to integrate the acoustic emission and strain data. For damage detection and severity assessment, a hybrid health indicator is obtained by feature-level fusion while a complementary and cooperative fusion of the diagnostic results is developed for damage localization and type identification. We show that damage growth can be monitored up until final failure, thereby performing a simultaneous damage assessment on all four SHM levels. In this manner, we demonstrate that by proposing a fusion-based approach toward SHM of composite structures, the intrinsic capacity of each SHM technique can be utilized, leading to synergistic effects for damage diagnostics.

Topics
  • impedance spectroscopy
  • surface
  • Carbon
  • strength
  • fatigue
  • composite
  • impact test
  • acoustic emission