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 (2/2 displayed)

  • 2022Process and feedstock driven microstructure for laser powder bed fusion of 316L stainless steel14citations
  • 2022Optimization of stochastic feature properties in laser powder bed fusion10citations

Places of action

Chart of shared publication
Koepke, Josh R.
1 / 2 shared
Jared, Bradley H.
2 / 8 shared
Saiz, David J.
2 / 6 shared
Dickens, Sara M.
1 / 1 shared
Jensen, Scott C.
2 / 2 shared
Boyce, Brad L.
1 / 8 shared
Carroll, Jay D.
1 / 2 shared
Koepke, Joshua R.
1 / 1 shared
Chart of publication period
2022

Co-Authors (by relevance)

  • Koepke, Josh R.
  • Jared, Bradley H.
  • Saiz, David J.
  • Dickens, Sara M.
  • Jensen, Scott C.
  • Boyce, Brad L.
  • Carroll, Jay D.
  • Koepke, Joshua R.
OrganizationsLocationPeople

article

Optimization of stochastic feature properties in laser powder bed fusion

  • Heiden, Michael J.
  • Boyce, Brad L.
  • Jared, Bradley H.
  • Carroll, Jay D.
  • Saiz, David J.
  • Koepke, Joshua R.
  • Jensen, Scott C.
Abstract

Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. As such, in this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm<sup>2</sup> in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.

Topics
  • density
  • impedance spectroscopy
  • pore
  • surface
  • energy density
  • stainless steel
  • melt
  • strength
  • selective laser melting
  • porosity
  • ductility