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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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2023Measuring and Predicting the Effects of Residual Stresses from Full-Field Data in Laser-Directed Energy Deposition4citations

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Polyzos, Efstratios
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Ertveldt, Julien
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Pyl, Lincy
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Van Hemelrijck, Danny
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Pulju, Hendrik
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2023

Co-Authors (by relevance)

  • Polyzos, Efstratios
  • Ertveldt, Julien
  • Pyl, Lincy
  • Van Hemelrijck, Danny
  • Pulju, Hendrik
  • Hinderdael, Michaël
OrganizationsLocationPeople

article

Measuring and Predicting the Effects of Residual Stresses from Full-Field Data in Laser-Directed Energy Deposition

  • Polyzos, Efstratios
  • Ertveldt, Julien
  • Pyl, Lincy
  • Mäckel, Peter
  • Van Hemelrijck, Danny
  • Pulju, Hendrik
  • Hinderdael, Michaël
Abstract

This article presents a novel approach for assessing the effects of residual stresses in laser directed energy deposition (L-DED). The approach focuses on exploiting the potential of rapidly growing tools such as machine learning and polynomial chaos expansion for handling full-field data for measurements and predictions. In particular, the thermal expansion coefficient of thin-wall L-DED<br/>steel specimens is measured and then used to predict the displacement fields around the drilling hole in incremental hole-drilling tests. The incremental hole-drilling test is performed on cubic L-DED steel specimens and the displacement fields are visualized using a 3D micro-digital image correlation setup. A good agreement is achieved between predictions and experimental measurements.

Topics
  • Deposition
  • impedance spectroscopy
  • steel
  • thermal expansion
  • directed energy deposition
  • machine learning