## 1 Introduction  
Metal additive manufacturing (AM), a disruptive fabrication technology, offers significant advantages in material utilization and manufacturing cycle reduction. It has shown great potential for applications in strategic emerging industries such as aerospace, rail transportation, and biomedical engineering [1]. However, due to the inherent characteristics of the layer-by-layer deposition process and repeated thermal cycling, various types of internal defects are inevitably introduced during fabrication, among which void defects, in the form of gas pores or lack-of-fusion (LOF) cavities, are particularly prevalent [2]. These voids can critically compromise the structural integrity and service life of AM components, and in severe cases, may even lead to catastrophic failures [3]. Therefore, the detection and characterization of void defects during the AM process have become essential for ensuring the quality and reliability of manufactured parts. Conventional quality assurance in metal AM has predominantly relied on post-process non-destructive testing (NDT) methods. These techniques, commonly termed offline or ex situ inspections, are conducted after the fabrication process to evaluate the structural soundness and reliability of the final component [4]. Representative offline NDT methods include ultrasonic testing (UT) [5; 6], radiographic testing (RT) [7; 8], eddy current testing (ET) [9; 10], and liquid penetrant testing (PT) [11; 12]. Each of these techniques offers specific benefits and faces inherent limitations [13; 14]. For example, UT is capable of detecting internal defects with considerable penetration depth and sensitivity, but it often necessitates the use of couplants and meticulous surface preparation, thereby reducing its practicality for high-volume or complex part geometries. RT methods, including X-ray imaging and computed tomography, deliver high-resolution visualizations of internal features but are often restricted by high equipment and operational costs, prolonged acquisition times, and strict safety protocols--challenges that become increasingly prominent when inspecting large-scale or dense metallic structures. ET is highly effective for detecting surface and near-surface flaws in conductive materials, though its sensitivity declines with increasing depth and geometric complexity. PT, on the other hand, is a cost-effective technique suitable for identifying surface-breaking discontinuities regardless of material type, but its application is restricted to open-to-surface defects and requires careful surface cleaning and post-inspection processing. Furthermore, a fundamental limitation shared across these offline techniques is their retrospective nature: because they are implemented only after the completion of the build, they cannot support timely process correction. As a result, defect discovery at this stage may lead to substantial material waste, increased rework or scrapping costs, and the inefficient use of manufacturing resources [15]. To address the limitations of offline inspection, extensive research efforts have been directed toward the development of in situ or online defect detection technologies capable of monitoring part quality during the fabrication process itself. These online monitoring approaches enable the identification of defects as they emerge, offering the potential for immediate feedback and process intervention [16]. Generally, online detection methods fall into two major categories: process signature monitoring and real-time non-destructive evaluation. The former involves indirect monitoring through physical indicators such as melt pool dimensions, thermal gradients, spatter activity, and deposition morphology, typically measured via optical, thermal, or acoustic sensors [17; 18].