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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SINTEF Industry

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (4/4 displayed)

  • 2024CFD modeling for predicting imperfections in laser welding and additive manufacturing of aluminum alloyscitations
  • 2021Wire arc additive manufacturing of AA5183 with TiC nanoparticles22citations
  • 2008A study of large plastic deformations in dual phase steel using digital image correlation and FE analysis97citations
  • 2001Effects of plastic deformation and stresses on dilatation during the martensitic transformation in a B-bearing steelcitations

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Chart of shared publication
Hovig, Even Wilberg
1 / 6 shared
Zhang, Kai
1 / 1 shared
Bunaziv, Ivan
1 / 20 shared
Ma, Xiang
1 / 5 shared
Brizuela, Omar Emmanuel Godinez
1 / 1 shared
Ren, Xiaobo
1 / 16 shared
Skjetne, Paal
1 / 3 shared
Roven, Hans Jørgen
1 / 8 shared
Langelandsvik, Geir
1 / 9 shared
Akselsen, Odd M.
1 / 2 shared
Clausen, Arild H.
1 / 1 shared
Tarigopula, Venkat
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Hild, François
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Hopperstad, Odd Sture
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Lademo, Odd-Geir
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Langseth, Magnus
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Oldenburg, Mats
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Karjalainen, L. P.
1 / 17 shared
Somani, M. C.
1 / 28 shared
Chart of publication period
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2021
2008
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Co-Authors (by relevance)

  • Hovig, Even Wilberg
  • Zhang, Kai
  • Bunaziv, Ivan
  • Ma, Xiang
  • Brizuela, Omar Emmanuel Godinez
  • Ren, Xiaobo
  • Skjetne, Paal
  • Roven, Hans Jørgen
  • Langelandsvik, Geir
  • Akselsen, Odd M.
  • Clausen, Arild H.
  • Tarigopula, Venkat
  • Hild, François
  • Hopperstad, Odd Sture
  • Lademo, Odd-Geir
  • Langseth, Magnus
  • Oldenburg, Mats
  • Karjalainen, L. P.
  • Somani, M. C.
OrganizationsLocationPeople

article

CFD modeling for predicting imperfections in laser welding and additive manufacturing of aluminum alloys

  • Hovig, Even Wilberg
  • Zhang, Kai
  • Bunaziv, Ivan
  • Ma, Xiang
  • Brizuela, Omar Emmanuel Godinez
  • Ren, Xiaobo
  • Eriksson, Magnus
  • Skjetne, Paal
Abstract

<jats:p>Aluminum and its alloys are widely used in various applications including e-mobility applications due to their lightweight nature, high corrosion resistance, good electrical conductivity, and excellent processability such as extrusion and forming. However, aluminum and its alloys are difficult to process with a laser beam due to their high thermal conductivity and reflectivity. In this article, the two most used laser processes, i.e., laser welding and laser powder bed fusion (LPBF) additive manufacturing, for processing of aluminum have been studied. There are many common laser-material interaction mechanisms and challenges between the two processes. Deep keyhole mode is a preferred method for welding due to improved productivity, while a heat conduction mode is preferred in LPBF aiming for zero-defect parts. In LPBF, the processing maps are highly desirable to be constructed, which shows the transition zone. Presented numerical modeling provides a more in-depth understanding of porosity formation, and different laser beam movement paths have been tested including circular oscillation paths. High accuracy processing maps can be constructed for LPBF that allows us to minimize tedious and time-consuming experiments. As a result, a modeling framework is a highly viable option for the cost-efficient optimization of process parameters.</jats:p>

Topics
  • impedance spectroscopy
  • corrosion
  • mobility
  • experiment
  • extrusion
  • aluminium
  • selective laser melting
  • defect
  • porosity
  • thermal conductivity
  • electrical conductivity