## 1 Introduction  
Metallic laser powder bed fusion (LPBF) additive manufacturing (AM) is a layer-based approach in which near-net shape parts are built directly from digital files [1]. In situ process monitoring sensors are used to obtain real-time information of the build process with the goal to connect in situ process signatures and post-process part quality [2]. Complex structures similar to overhangs imply manufacturing challenges due to the prevalence of distortion and dress defects. There is a large difference in the thermal conductivity of a solidified layer to the one of powder (powder only about 1%) [3, 4]. Moreover, an unsupported overhang has no direct connection to the previously built layers or the build platform, and thus may be exposed to a lower cooling rate than the main structure. This can cause high thermal gradients resulting in differing material expansion behavior in areas of raised or reduced temperatures during the process of fast melting and solidification. This causes thermal stress and distortion [5, 6, 7, 8]. These thermal behaviors were, for example, studied in simulation and experiment of forming T-shape overhangs [9]. [PERSON] et al. [10], with varying process parameters, printed overhanging structures and examined the dimensional error statistically. They not only showed that the combined process parameters characterize the process, but especially that the energy density impacts the cross formation via an overheated overhang zone. Recently, [PERSON] et al. published a wide-ranging data set comparing measured and simulated temperatures in the fabrication of overhang structures. They also emphasized quality features such as microstructure, strain, and distortion directly related to the thermal history that the part was exposed to and that accurate models of those phenomena likely depend on accurate, validated, thermal process models [11]. During the LPBF process, glow can be seen due to laser energy deposition at the current processing position, which contains the three components, namely reflected laser radiation, plasma emission, and temperature radiation. The optical tomography (OT)-based monitoring system was first introduced in [12] by filtering the reflection of laser radiation and plasma emission, which shows the possibility of monitoring temperature radiation from the building platform during the LPBF process over time. The captured radiation has positive correlation with melt pool temperature, according to the black body testing. To monitor the thermal behaviors, the thermal modelling of the LPBF process can be classified into three categories: experimental measurement, numerical modeling, and analytical modeling. Experiment techniques apply sensors, e.g., pyrometer [13] and thermal camera [14], to measure the temperature during the process, which is inconvenient without manufacturing the parts. In the meantime, finite element analysis (FEM)-based numerical models and physical-based analytical models were introduced to predict temperature behavior. The FEM-based numerical models are reviewed in [15]. The main drawback of this is that, for large-scale parts, the computational time is high due to the iterative computation. Analytical models, which predict the temperature along the laser path without iterative computation, are introduced in [16; 17]. However, the numerical models need the detailed material and process parameters for accurate prediction, which can be different for varied materials and machines. Apart from numerical and analytical methods, machine learning (ML) methods enable handling AM process monitoring data with high dimensionality and complexity to predict the process behaviors in a data-driven manner. While there is an increase in available data in the AM lifecycle, [PERSON] et al. [18] state that there is still restricted knowledge in describing AM material-geometry-process-structure-property-performance relationships, and thus call for further research regarding computational and analytical tools.