X-ray imaging has become essential for understanding powder-based metal processing. In solid-state sintering, synchrotron tomography reveals particle rearrangement, neck growth, and pore evolution, clarifying how packing heterogeneity and particle-size distribution govern densification. In laser powder bed fusion, high-speed radiography captures microsecond-scale melt-pool behavior, including keyhole dynamics, vapor-jet entrainment, spatter formation, and bubble-mediated porosity, thereby enabling mechanistic links between processing conditions and defect generation. Nonetheless, current X-ray methods face trade-offs between spatial and temporal resolution and often remain qualitative. Integrating operando imaging with physics-based simulations and machine-learning models offers a path toward quantitative prediction and real-time control. This review summarizes recent progress and highlights key challenges and opportunities for advancing operando characterization of powder-based metal processes.
Cho et al. (Thu,) studied this question.