# In Situ X-Ray Imaging and Machine Learning in Ultrasonic Field-Assisted Laser-Based Additive Manufacturing: A Review

## TL;DR

This review explores how X-ray imaging and machine learning can improve ultrasonic-assisted laser additive manufacturing to reduce defects in metal components.

## Contribution

The paper systematically reviews X-ray studies and ML applications in ultrasonic field-assisted AM, identifying key challenges and future directions.

## Key findings

- X-ray imaging reveals melt pool dynamics and defect suppression in ultrasonic-assisted AM.
- Machine learning enables real-time monitoring and defect prediction in additive manufacturing.
- Physics-informed models and multimodal diagnostics are promising for advancing UF-LBAM.

## Abstract

Metal additive manufacturing (AM) offers unprecedented opportunities to fabricate complex, lightweight metallic components, yet its practical deployment remains fundamentally constrained by defects arising from rapid melting and solidification. Cyclic thermal transients generate cracks, pores, residual stresses, and lack-of-fusion regions, undermining mechanical performance and reliability. Ultrasonic field-assisted laser-based additive manufacturing (UF-LBAM) has emerged as a powerful approach to manipulate melt pool dynamics and suppress defect formation. Nevertheless, the governing physical mechanisms remain poorly understood, particularly under highly non-equilibrium ultrasonic excitation, where acoustic pressure oscillations, melt convection, cavitation, and solidification are intricately coupled across multiple temporal and spatial scales. Here, we provide a systematic review of X-ray based fundamental studies in UF-LBAM and the diverse applications of machine learning (ML), detailing the literature selection criteria and methodology. We highlight advances spanning synchrotron X-ray revealed physical phenomena, ML-driven real-time monitoring and defect prediction, and pathways toward industrial implementation. Critical challenges persist, including fundamental physics gaps, transferability of ML models across alloy systems, and real-time control limitations. We further identify promising directions for the field, such as physics-informed models, multimodal diagnostics, and closed-loop control, which together promise to unlock the full potential of UF-LBAM for high-performance metal component fabrication.