Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

Agrawal, Ankur K.; Rankouhi, Behzad; Thoma, Dan J.

doi:10.1016/j.cossms.2022.101024

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

Journal Article · Tue Jul 26 00:00:00 EDT 2022 · Current Opinion in Solid State and Materials Science

DOI:https://doi.org/10.1016/j.cossms.2022.101024· OSTI ID:1895149

Agrawal, Ankur K. ^[1]; Rankouhi, Behzad ^[2]; Thoma, Dan J. ^[2]

Univ. of Wisconsin, Madison, WI (United States); University of Wisconsin-Madison (UW)
Univ. of Wisconsin, Madison, WI (United States)

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

View Accepted Manuscript (DOE)

Research Organization:: Georgia Institute of Technology, Atlanta, GA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: NA0003921

OSTI ID:: 1895149

Journal Information:: Current Opinion in Solid State and Materials Science, Journal Name: Current Opinion in Solid State and Materials Science Journal Issue: 6 Vol. 26; ISSN 1359-0286

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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