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

Process and feedstock driven microstructure for laser powder bed fusion of 316L stainless steel

  • Koepke, Josh R.
  • Heiden, Michael J.
  • Jared, Bradley H.
  • Saiz, David J.
  • Dickens, Sara M.
  • Jensen, Scott C.
Abstract

Here in the pursuit of improving additively manufactured (AM) component quality and reliability, fine-tuning critical process parameters such as laser power and scan speed is a great first step toward limiting defect formation and optimizing the microstructure. However, the synergistic effects between these process parameters, layer thickness, and feedstock attributes (e.g. powder size distribution) on part characteristics such as microstructure, density, hardness, and surface roughness are not as well-studied. In this work, we investigate 316L stainless steel density cubes built via laser powder bed fusion (L-PBF), emphasizing the significant microstructural changes that occur due to altering the volumetric energy density (VED) via laser power, scan speed, and layer thickness changes, coupled with different starting powder size distributions. This study demonstrates that there is not one ideal process set and powder size distribution for each machine. Instead, there are several combinations or feedstock/process parameter ‘recipes’ to achieve similar goals. This study also establishes that for equivalent VEDs, changing powder size can significantly alter part density, GND density, and hardness. Through proper parameter and feedstock control, part attributes such as density, grain size, texture, dislocation density, hardness, and surface roughness can be customized, thereby creating multiple high-performance regions in the AM process space.

Topics
  • density
  • impedance spectroscopy
  • surface
  • energy density
  • grain
  • stainless steel
  • grain size
  • hardness
  • selective laser melting
  • dislocation
  • texture