4D reconstruction of alumina laser melt pools at 25 kHz via operando X-ray multi-projection imaging

TL;DR

This paper introduces rotation-XMPI, a novel 4D imaging technique that captures high-speed melt-pool dynamics in additive manufacturing by combining multi-projection imaging with deep learning, achieving unprecedented imaging speeds.

Contribution

The paper presents rotation-XMPI, a method that decouples temporal resolution from sample rotation speed, enabling ultrafast 4D imaging of melt pools in additive manufacturing.

Findings

Achieves 25,000 reconstructed volumes per second.

Resolves melt-pool morphology and keyhole evolution.

Outperforms conventional tomography in speed and resolution.

Abstract

Advancing additive manufacturing, e.g., laser powder-bed fusion (LPBF), requires resolving rapid processes such as melt-pool dynamics and keyhole evolution in 4D (3D + time). Operando X-ray tomography is a state-of-the-art approach for 4D characterization, but its temporal resolution is fundamentally constrained by the sample rotation speed, limiting achievable 4D imaging rates and preventing the resolution of these fast phenomena. Here we present rotation-enabled X-ray Multi-Projection Imaging (rotation-XMPI), which captures three angularly resolved projections per time step and thereby decouples temporal resolution from the sample rotation speed. Combined with a self-supervised deep-learning reconstruction framework for multi-angle inputs, rotation-XMPI enables high-fidelity 4D imaging at unprecedented speed. We demonstrate the approach in an operando alumina laser-remelting experiment at MAX IV using three beamlets combined with 25 Hz sample rotation. Rotation-XMPI resolves melt-pool morphology and keyhole evolution; in contrast, conventional and limited-angle tomography remain rotation-limited, and motion blur prevents resolving these dynamics. Overall, rotation-XMPI delivers a 250-fold increase relative to state-of-the-art melt-pool imaging, effectively achieving 25,000 reconstructed volumes per second. This method establishes a practical route to scalable ultrafast 4D imaging for additive manufacturing and other materials processes.