In situ melt pool measurements for laser powder bed fusion using multi sensing and correlation analysis

Laser powder bed fusion is a promising technology for local deposition and microstructure control, but it suffers from defects such as delamination and porosity due to the lack of understanding of melt pool dynamics. To study the fundamental behavior of the melt pool, both geometric and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the evolution of the melt pool shape, keyhole, vapor plume, and thermal evolution in Ti-6Al-4V and 410 stainless steel spot melt cases. Aside from presenting the sensing capability, this paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Preprocessing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradients, and cooling rates are performed, enabling future comprehensive melt pool dynamics and microstructure analysis. The study discovers a strong correlation between the thermal and X-ray data, demonstrating the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using costly synchrotron X-ray imaging. Such correlation can be used for future thermal-based melt pool control and model validation.

Automated summary

This study applies and integrates three advanced sensing technologies, namely synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera, to investigate the behavior of the melt pool in laser powder bed fusion. It presents a novel algorithm for accurately identifying melt pool geometries based on high-speed X-ray imaging data and implements preprocessing methods for IR data to estimate emissivity values. Quantifications on various parameters are conducted, revealing the strong correlation between thermal and X-ray data. The findings demonstrate the feasibility of using relatively inexpensive IR cameras for predicting features that currently require costly synchrotron X-ray imaging.