# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 1 Introduction  
Laser powder bed fusion (LPBF) technology, as an advanced metal additive manufacturing technique, has been widely applied in various strategic fields including automotive, aerospace, and biomedical industries. As LPBF technology is a layer-by-layer additive process, the structural quality of parts is significantly influenced by the surface quality of intermediate layers: intermediate layers with poor surface quality and high roughness may lead to poor interlayer consolidation, reducing the overall density of the part, and, in severe cases, can cause the occurrence of large-scale through-layer defects within the part, such as porosity, lack of fusion, interlayer inclusion, cracks, and delamination [1]. Therefore, analyzing the surface morphology of intermediate layers during the LPBF process, as well as the mechanisms and influencing factors behind their formation, to subsequently enhance the overall build quality of parts, has become one of the current important research directions. Studies indicate that the surface formation quality of intermediate layers during the LPBF process is influenced by a multitude of process parameters, including laser power, scanning speed, hatch spacing, scanning strategy, and powder layer thickness [2; 3; 4; 5; 6; 7]. Researchers have investigated the formation patterns of LPBF surface topography through experiments with various combinations of process parameters. As the laser moves, the rapid heating and cooling solidification of the local area of the powder bed result in significant temperature gradients in the melt pool and surrounding areas, leading to uneven thermal contraction [8]. When the laser energy density input is low, the metal powder does not melt sufficiently, failing to form a continuous and stable melt pool. After cooling, the discontinuous molten segments further contract into surface balling defects, severely reducing the quality of the layer surface [9]. Conversely, when the energy density is too high, the metal in the center of the melt pool may vaporize due to excessive temperatures, and the high-pressure steam forces the liquid metal towards the edges of the melt pool, creating keyhole defects [10; 11]. Therefore, the process of metal powder melting upon heating to form a melt pool and the rapid solidification of the melt pool into a solid mass has the most direct and intrinsic impact on the surface quality of LPBF-printed layers [12]. Monitoring and analyzing this stage can help quickly assess the surface quality of the current layer, providing a reliable theoretical basis for suppressing the occurrence of surface defects such as high surface roughness, high surface waviness, uneven surfaces, and balling particles, thereby enhancing the surface quality of intermediate layers. Researchers typically collect signals such as visual, acoustic, and thermal ones to monitor the LPBF process, with visual signal monitoring offering significant advantages like intuitive results and rich information, making it a current research hotspot [13; 14]. Among various visual signal monitoring methods, synchronized XCT (X-ray computed tomography) equipment is costly and slow in imaging speed; optical or thermal imaging cameras mounted on a side axis typically suffer from limited angles of view, narrow fields of view, low resolution, short monitoring durations, and an inability to continuously track the monitored objects, among other issues [15; 16]. Traditional coaxial monitoring methods are usually based on a single visual sensor, resulting in monitoring outcomes that lack richness in information, which is not conducive to research at the fundamental level [17; 18]. To address this issue, some studies employ a setup that combines coaxial and off-axis cameras to synchronously capture various monitoring data, including infrared and visible light [19]. Other studies have installed both visible light and infrared cameras in a coaxial manner to avoid the shortcomings associated with off-axis monitoring.