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

• Published in: Current opinion in solid state & materials science Vol. 26; no. 6; p. 101024
• Main Authors: Agrawal, Ankur K., Rankouhi, Behzad, Thoma, Dan J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-12-2022; Elsevier
• Subjects: Additive manufacturing; Analytical models; Defects; Laser powder bed fusion; Laser-metal interaction; MATERIALS SCIENCE; Melt pool geometry; Processing maps; Laser powder bed fusion; Analytical models; Laser-metal interaction; Additive manufacturing; Processing maps; Defects; Melt pool geometry
• ISSN: 1359-0286
• DOI: 10.1016/j.cossms.2022.101024

•Laser powder bed fusion (LPBF) is evaluated in terms of existing analytical models defining optimum processing parameters.•Power-velocity (PV) maps provide a rapid visualization of analytical models defining porosity defect regions in LPBF.•Analytical models based on melt pool geometries may be further refined with in situ diagnostics of the melt pool.•Analytical and experimental examples are presented to demonstrate the predictive capability of the methodology.•The PV maps provide a basis for subsequent structure/property investigations nominally within the predicted boundaries.
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. 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.