## 1 Introduction  
The unmatched ability of metal additive manufacturing (AM) to produce customized parts with complex geometries has led to increased demand for these processes. According to the ISO/ASTM 52900 international standard, the most adapted metal AM technologies are directed energy deposition (DED) and powder bed fusion (PBF). Both processes use a laser or an electron beam to selectively melt the metal and build the part in a layer-by-layer method. Powder bed fusion (PBF) processes include direct metal laser deposition (DMLS), selective laser sintering (SLS), selective laser melting (SLM), electron beam melting (EBM), and selective heat sintering (SHS) [1]. The laser powder bed fusion (L-PBF) process has better surface quality and higher dimensional accuracy than other metal AM processes [2]. The laser powder bed fusion (L-PBF) process produces complex geometries and lattice structures with high feature resolution [3, 4]. Compared to conventional manufacturing processes, AM processes do not use expensive tooling or dies, which helps to reduce the lead time for parts manufactured using AM processes [5]. Moreover, L-PBF processes help reduce the need for assembly, thus decreasing the total number of parts in a single component [6]. The capabilities for weight reduction have been the main driver for aerospace industries to invest in L-PBF processes heavily [4]. Furthermore, the ability to customize implants to each patient-specific anatomy and produce a complex lattice structure was vital for the biomedical industry's interest in L-PBF parts [7]. In addition, the tools and dies industries have used the advantages of the L-PBF process to manufacture dies with conformal cooling channels, which significantly improves the performance of these components [8]. Instead of having traditional drilled cooling channels, the cooling channels are custom-made to each cavity, thus reducing cooling and cycle times and improving the overall process performance. Despite the many technological developments that have taken place in L-PBF processes in the past 20 years, they still suffer from poor process repeatability [9; 10]. Although some L-PBF components have already begun their certification routes, parts intended to be used in highly critical applications are still at the early stages of development and certifications [11]. Significant research effort has been directed towards experimentally optimizing the process parameters by understanding the process-structure-property relationships [12; 13; 14; 15], while other research studies mainly focused on physics-based numerical models to predict the properties of the manufactured parts and attempt to prevent defects [16; 17; 18]. Both experimental and numerical efforts have laid a good foundation for enhancing the understanding of the process. However, these efforts are time-consuming and computationally expensive [19]. To face the challenges of poor repeatability of L-PBF processes, developing in situ monitoring systems and feedback control strategies has been a priority research interest [20]. The ultimate objective of applying in situ L-PBF process monitoring is developing closed-loop feedback systems [21] that would be able to detect defects in a suitable response time to take corrective action and eventually improve the parts' repeatability and reproducibility. The development of sensor technologies has led to a significant increase in the amount and dimensionality of data collected during the L-PBF [22]. An operator will not be capable of manually screening and modeling the massive amount of data using statistical methods or a design of experiment approach. As such, a suitable approach to overcome the challenges in handling the L-PBF data is applying artificial intelligence (AI) solutions such as machine learning (ML) methods [23]. ML algorithms help identify patterns and regularities in large datasets with limited process knowledge. The models learn from data without the need for explicit programming and thus are suitable for in situ monitoring of L-PBF processes.