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

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 condi-tions. 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 pre-sents an assessment of existing analytical equations and models that provide an estimate of the melt pool ge-ometry 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) experi-ments 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.

Automated summary

This study evaluates the existing analytical equations and models used in the laser powder bed fusion (LPBF) process and provides a quick approximation method for calculating power-velocity (PV) processing maps for various materials, by combining the melt pool equations with defect criteria. These predictive processing maps can be used for designing optimal processing parameters and compared with experimental data.