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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Technical University of Denmark

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2021Quantifying effects of manufacturing methods on fiber orientation in unidirectional composites using structure tensor analysis38citations
  • 2020Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor14citations

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Chart of shared publication
Mikkelsen, Lars Pilgaard
2 / 71 shared
Dahl, Vedrana Andersen
2 / 10 shared
Dahl, Anders Bjorholm
2 / 18 shared
Jeppesen, N.
2 / 4 shared
Chart of publication period
2021
2020

Co-Authors (by relevance)

  • Mikkelsen, Lars Pilgaard
  • Dahl, Vedrana Andersen
  • Dahl, Anders Bjorholm
  • Jeppesen, N.
OrganizationsLocationPeople

article

Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor

  • Mikkelsen, Lars Pilgaard
  • Dahl, Vedrana Andersen
  • Christensen, Anders Nymark
  • Dahl, Anders Bjorholm
  • Jeppesen, N.
Abstract

The mechanical properties of composite fiber materials are highly dependent on the orientation of the fibers. Micro-CT enables acquisition of high-resolution 3D images, where individual fibers are visible. However, manually extracting orientation information from the samples is impractical due to the size of the 3D images. In this paper, we use a Structure Tensor to extract orientation information from a large 3D image of non-crimp glass fiber fabric. We go through the process of segmenting the image and extracting the orientation distribution step-by-step using structure tensor and show the results of the analysis of the studied non-crimp fabric. The Jupyter notebooks and Python code used for the data-analysis are publicly available, detailing the process and allowing the reader to use the method on their own data. The results show that structure tensor analysis works well for determining fiber orientations, which has many useful applications.

Topics
  • impedance spectroscopy
  • glass
  • glass
  • composite