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

Marques, Mc

  • Google
  • 1
  • 4
  • 5

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2021The influence of infill density gradient on the mechanical properties of PLA optimized structures by additive manufacturing5citations

Places of action

Chart of shared publication
Pais, Ail
1 / 1 shared
Silva, C.
1 / 69 shared
Belinha, J.
1 / 22 shared
Alves, Jl
1 / 19 shared
Chart of publication period
2021

Co-Authors (by relevance)

  • Pais, Ail
  • Silva, C.
  • Belinha, J.
  • Alves, Jl
OrganizationsLocationPeople

article

The influence of infill density gradient on the mechanical properties of PLA optimized structures by additive manufacturing

  • Pais, Ail
  • Silva, C.
  • Marques, Mc
  • Belinha, J.
  • Alves, Jl
Abstract

The aim of this work is the development of a novel framework for structural optimization using bio-inspired remodelling algorithm adapted to additive manufacturing. The fact that polylactic acid (PLA, E = 3145 MPa (Young's modulus) according to the supplier for parts obtained by injection) shows a similar parameterized behavior with ductile metals, in the sense that both materials are characterized by a bi-linear elastic-plastic law, allows to simulate and prototype parts to be further constructed in ductile metals at a lower cost and then be produced with more expensive fabrication processes. Moreover, cellular materials allow for a significant weight reduction and therefore reduction of production costs. Structural optimization algorithms based on biological phenomena were used to determine the density distribution of the infill density of the specimens. Several simple structures were submitted to distinct complex load cases and analyzed using the mentioned optimization algorithms combined with the finite element method and a meshless method. The surface was divided according to similar density and then converted to stereolitography files and infilled with the gyroid structure at the desired density determined before, using open-source slicing software. Smoothing functions were used to smooth the density field obtained with the remodeling algorithms. The samples were printed with fused filament fabrication technology and submitted to mechanical flexural tests similar to the ones analyzed analytically, namely three- and four-point bending tests. Thus, the factors of analysis were the smoothing parameter and the remodeling method, and the responses evaluated were stiffness, specific stiffness, maximum force, and mass. The experimental results correlated (obtaining accuracy of 35% for the three-point bending load case and 5% for the four-point bending load case) to the numerical results in terms of flexural stiffness and it was found that the complexity of the load case is relevant for the efficiency of the functional gradient. The fused filament fabrication process is still not accurate enough to be able to experimentally compare the results based of finite element method and meshless method analyses.

Topics
  • density
  • impedance spectroscopy
  • surface
  • polymer
  • bending flexural test
  • additive manufacturing
  • gyroid