Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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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.

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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.

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Karakoç, Alp

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Aalto University

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (18/18 displayed)

  • 2024Design, Fabrication, and Characterization of 3D-Printed Multiphase Scaffolds Based on Triply Periodic Minimal Surfaces3citations
  • 2023Effects of leaflet curvature and thickness on the crimping stresses in transcatheter heart valve2citations
  • 2023Low-cost thin film patch antennas and antenna arrays with various background wall materials for indoor wireless communications8citations
  • 2022Predicting the upper-bound of interlaminar impact damage in structural composites through a combined nanoindentation and computational mechanics technique9citations
  • 2022Simplified indentation mechanics to connect nanoindentation and low-energy impact of structural composites and polymerscitations
  • 2021Effect of single-fiber properties and fiber volume fraction on the mechanical properties of Ioncell fiber composites8citations
  • 2021Exploring the possibilities of FDM filaments comprising natural fiber-reinforced biocomposites for additive manufacturing26citations
  • 2021Mild alkaline separation of fiber bundles from eucalyptus bark and their composites with cellulose acetate butyrate14citations
  • 2020Data-Driven Computational Homogenization Method Based on Euclidean Bipartite Matching7citations
  • 2020Mechanical and thermal behavior of natural fiber-polymer composites without compatibilizers4citations
  • 2020A predictive failure framework for brittle porous materials via machine learning and geometric matching methods15citations
  • 2020Comparative screening of the structural and thermomechanical properties of FDM filaments comprising thermoplastics loaded with cellulose, carbon and glass fibers30citations
  • 2020Comparative screening of the structural and thermomechanical properties of FDM filaments comprising thermoplastics loaded with cellulose, carbon and glass fibers30citations
  • 2019Machine Learning assisted design of tailor-made nanocellulose films27citations
  • 2018Stochastic fracture of additively manufactured porous composites33citations
  • 2016Shape and cell wall slenderness effects on the stiffness of wood cell aggregates in the transverse plane1citations
  • 2016Modeling of wood-like cellular materials with a geometrical data extraction algorithm1citations
  • 2013Effective stiffness and strength properties of cellular materials in the transverse plane61citations

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Norris, Nicholas
1 / 1 shared
Vigil, Josette
1 / 1 shared
Becker, Timothy A.
1 / 1 shared
Lewis, Kailey
1 / 1 shared
Taciroğlu, Ertuğrul
1 / 1 shared
Aksoy, Olcay
1 / 1 shared
Jäntti, Riku
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Mela, Lauri
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Xie, Boxuan
1 / 1 shared
Kerminen, Juho
1 / 1 shared
Ruttik, Kalle
1 / 1 shared
Ning, Haibin
1 / 9 shared
Flores, Mark
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Taciroglu, Ertugrul
3 / 4 shared
Xu, L. Roy
1 / 1 shared
Islam, Md Shariful
1 / 2 shared
Martinez, Ricardo
1 / 2 shared
Zhao, Kai
1 / 1 shared
Xu, Luoyu R.
1 / 1 shared
Bulota, Mindaugas
1 / 4 shared
Hummel, Michael
1 / 28 shared
Sixta, Herbert
1 / 22 shared
Paltakari, Jouni
8 / 10 shared
Sriubaitė, Simona
1 / 1 shared
Hughes, Mark
1 / 14 shared
Abidnejad, Roozbeh
1 / 6 shared
Ranta, Anton
1 / 3 shared
Rafiee, Mahdi
1 / 1 shared
Ojha, Krishna
1 / 1 shared
Vuorinen, Tapani
1 / 9 shared
Dou, Jinze
1 / 2 shared
Evtyugin, Dmitry
1 / 1 shared
Hietala, Sami
1 / 19 shared
Johansson, Ls
1 / 8 shared
Sajaniemi, Veikko
1 / 1 shared
Keleş, Özgür
2 / 2 shared
Rastogi, Vibhore K.
2 / 2 shared
Isoaho, Tapani
2 / 2 shared
Rojas, Orlando J.
1 / 51 shared
Tardy, Blaise
2 / 4 shared
Wiklund, Jenny
1 / 1 shared
Borghei, Maryam
1 / 16 shared
Özkan, Merve
1 / 1 shared
Gelb, Jeff
1 / 2 shared
Huynh, Jimmy
1 / 1 shared
Anderson, Eric H.
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Freund, Jouni
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Sjölund, Johanna
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Hernandez-Estrada, Albert
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Reza, Mehedi
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Co-Authors (by relevance)

  • Norris, Nicholas
  • Vigil, Josette
  • Becker, Timothy A.
  • Lewis, Kailey
  • Taciroğlu, Ertuğrul
  • Aksoy, Olcay
  • Jäntti, Riku
  • Mela, Lauri
  • Xie, Boxuan
  • Kerminen, Juho
  • Ruttik, Kalle
  • Ning, Haibin
  • Flores, Mark
  • Taciroglu, Ertugrul
  • Xu, L. Roy
  • Islam, Md Shariful
  • Martinez, Ricardo
  • Zhao, Kai
  • Xu, Luoyu R.
  • Bulota, Mindaugas
  • Hummel, Michael
  • Sixta, Herbert
  • Paltakari, Jouni
  • Sriubaitė, Simona
  • Hughes, Mark
  • Abidnejad, Roozbeh
  • Ranta, Anton
  • Rafiee, Mahdi
  • Ojha, Krishna
  • Vuorinen, Tapani
  • Dou, Jinze
  • Evtyugin, Dmitry
  • Hietala, Sami
  • Johansson, Ls
  • Sajaniemi, Veikko
  • Keleş, Özgür
  • Rastogi, Vibhore K.
  • Isoaho, Tapani
  • Rojas, Orlando J.
  • Tardy, Blaise
  • Wiklund, Jenny
  • Borghei, Maryam
  • Özkan, Merve
  • Gelb, Jeff
  • Huynh, Jimmy
  • Anderson, Eric H.
  • Freund, Jouni
  • Sjölund, Johanna
  • Hernandez-Estrada, Albert
  • Reza, Mehedi
OrganizationsLocationPeople

article

Data-Driven Computational Homogenization Method Based on Euclidean Bipartite Matching

  • Karakoç, Alp
  • Taciroglu, Ertugrul
  • Paltakari, Jouni
Abstract

Image processing methods combined with scanning techniques - for example, microscopy or microtomography - are now frequently being used for constructing realistic microstructure models that can be used as representative volume elements (RVEs) to better characterize heterogeneous material behavior. As a complement to those efforts, the present study introduces a computational homogenization method that bridges the RVE and material-scale properties in situ. To define the boundary conditions properly, an assignment problem is solved using Euclidean bipartite matching through which the boundary nodes of the RVE are matched with the control nodes of the rectangular prism bounding the RVE. The objective is to minimize the distances between the control and boundary nodes, which, when achieved, enables the bridging of scale-based features of both virtually generated and image-reconstructed domains. Following the minimization process, periodic boundary conditions can be enforced at the control nodes, and the resultingboundary value problem can be solved to determine the local constitutive material behavior. To verify the proposed method, virtually generated domains of closed-cell porous, spherical particle-reinforced, and fiber-reinforced composite materials are analyzed, and the results are compared with analytical Hashin-Shtrikman and Halpin-Tsai methods. The percent errors are within the ranges from 0.04% to 3.3%, from 2.7% to 14.9%, and from 0.5% to 13.2% for porous, particle-reinforced, and fiber-reinforced composite materials, respectively, indicating that the method has promising potential in the fields of image-based material characterization and computational homogenization. ; Peer reviewed

Topics
  • porous
  • impedance spectroscopy
  • microstructure
  • homogenization
  • fiber-reinforced composite
  • microscopy