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

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

doi:10.1016/j.mtla.2022.101356

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

Journal Article · Sun Feb 06 23:00:00 EST 2022 · Materialia

DOI:https://doi.org/10.1016/j.mtla.2022.101356· OSTI ID:1855797

Heiden, Michael J. ^[1]; Jensen, Scott C. ^[1]; Koepke, Josh R. ^[1]; Saiz, David J. ^[1]; Dickens, Sara M. ^[1]; Jared, Bradley H. ^[2]

Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
Univ. of Tennessee, Knoxville, TN (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1855797

Alternate ID(s):: OSTI ID: 1962148

Report Number(s):: SAND2022-2187J; 703695

Journal Information:: Materialia, Journal Name: Materialia Vol. 21; ISSN 2589-1529

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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