Optimization of stochastic feature properties in laser powder bed fusion

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

doi:10.1016/j.addma.2022.102943

Optimization of stochastic feature properties in laser powder bed fusion

Journal Article · Fri Jun 03 00:00:00 EDT 2022 · Additive Manufacturing

DOI:https://doi.org/10.1016/j.addma.2022.102943· OSTI ID:1877116

Jensen, Scott C. ^[1]; Koepke, Joshua R. ^[1]; Saiz, David J. ^[1]; Heiden, Michael J. ^[1]; Carroll, Jay D. ^[1]; Boyce, Brad L. ^[1]; Jared, Bradley H. ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Univ. of Tennessee, Knoxville, TN (United States)

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 mm² 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.

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:: 1877116

Alternate ID(s):: OSTI ID: 1962111

Report Number(s):: SAND2022-8155J; 707363

Journal Information:: Additive Manufacturing, Journal Name: Additive Manufacturing Vol. 56; ISSN 2214-8604

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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