Understanding coaxial photodiode-based multispectral pyrometer measurements at the overhang regions in laser powder bed fusion for part qualification

Coaxial photodiode monitoring sensors provide a digital signature at the melt pool level in laser powder bed fusion (L-PBF), facilitating faster part qualification. However, current signatures are plagued by significant noise and signal variation and show counterintuitive trends such as a decrease in measured temperature near overhang surfaces. This paper investigates the behavior of coaxial photodiode-based melt pool monitoring (PD-MPM) thermal measurements at overhang regions by conducting a combination of experiments and multiphysics simulations. High-speed in situ synchrotron X-ray imaging is coupled with a coaxial photodiode system to enable comparison of the observed melt pool phenomena and monitoring signals during double-track AlSi10Mg experiments. A multiphysics model is developed to simulate the melt pool dynamics and concurrent sensor signals throughout the process. We propose a surrogate model that clarifies the correlation between sensor signals and melt pool temperature. Both experimental and simulation results emphasize the significant impacts of laser energy and keyhole formation on solid-liquid interface discontinuities and PD-MPM signals. Results reveal that a boiling region with a relatively smaller projected area, as near an overhang, can lead to a decrease in the measured melt pool temperature, even when the true peak temperature remains constant, meaning that PD-MPM temperature measurements cannot be used to estimate absolute melt pool temperature. Consequently, in overhang regions, interaction between the melt pool and underlying powder results in an abruptly deforming melt pool and a pronounced decrease in both individual photodiode intensities and overall measured melt pool temperature. This work illustrates how to correctly interpret the coaxial photodiode monitoring signals in L-PBF by uncovering the intricate dynamics within the melt pool, particularly in challenging overhang regions, and pave the foundation for data-driven part qualification.