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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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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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2023On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision‐Based Tool1citations

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Chart of shared publication
Wegener, Thomas
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Meier, David
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Sick, Bernhard
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Decke, Jens
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Engelhardt, Anna
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Niendorf, Thomas
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Ragunathan, Rishan
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Chart of publication period
2023

Co-Authors (by relevance)

  • Wegener, Thomas
  • Meier, David
  • Sick, Bernhard
  • Decke, Jens
  • Engelhardt, Anna
  • Niendorf, Thomas
  • Ragunathan, Rishan
OrganizationsLocationPeople

article

On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision‐Based Tool

  • Wegener, Thomas
  • Meier, David
  • Sick, Bernhard
  • Decke, Jens
  • Dulig, Franz
  • Engelhardt, Anna
  • Niendorf, Thomas
  • Ragunathan, Rishan
Abstract

<jats:p>Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high‐cycle‐fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.</jats:p>

Topics
  • impedance spectroscopy
  • surface
  • polymer
  • fatigue