## 1 Introduction
Previous research reviewed the advantages of applying ML in general in manufacturing and highlighted its significance for monitoring and control applications [23; 24; 25]. The integration between ML algorithms and modern in situ sensors can provide an optimum solution for enhancing the quality of L-PBF processed parts to meet the requirements of critical applications. Several review papers have been published in the area of in situ monitoring and control of metal AM processes. Some review articles focused mainly on sensors integration with L-PBF processes. For example, [PERSON] et al. [20] focused on reporting efforts in monitoring and in situ metrology for metal additive manufacturing technologies. [PERSON] et al. [26] reviewed the types of process defects that can be monitored via process signatures. Other articles focused on the general application of ML algorithms in the additive manufacturing lifecycle. For example, [PERSON] et al. [27] provide a state-of-the-art review on the applications of ML techniques in various AM technologies. The authors focus on three categories, namely, design for additive manufacturing, different processes, and production. The authors concluded that most of the literature focuses on design and engineering, while very limited research covers science and technology aspects. Therefore, the authors suggested some new ML application subfields in AM, such as microstructure characterization, new material development, and topology optimization. [PERSON] et al. [22] identified the application of ML in the areas in the AM lifecycle, including design, process plan, build, postprocessing, and testing and validation. The authors identified that more research regarding in situ data fusion is needed, not just in terms of sensor data fusion, but also in correlating in situ and ex situ data. The authors also highlighted how ML models could be used in design for additive manufacturing to optimize the part mass, support structure, and build time. [PERSON] et al. [19] focused on describing the ML algorithms and classifications in AM technology in general. Examples from supervised and unsupervised ML models were compared, and the application of using these models was highlighted. The authors recommended investing more effort in ML algorithms that would be suitable for AM processes, specifically active learning techniques. It was found that limited review articles cover the application of ML for in situ monitoring and control of L-PBF processes [28]. In addition, as various research is published on the application of ML in monitoring the L-PBF process, there was a need to summarize and compare the relationships of different sensors and the most common ML algorithms used in this area. This study gives an overview of the need for in situ monitoring of L-PBF and how different sensors can monitor different process signatures. Furthermore, it highlights how ML algorithms can be used to assess the collected data and generate corrective signals to the process. This paper also classifies the ML algorithms used in L-PBF process monitoring according to sensor types and highlights the different process signatures that can be monitored in each case. The article is organized as follows: first, an overview of the L-PBF process, process signatures, and defects are described. Then, a description of the different sensors that are integrated into the L-PBF machine is given. Then, a theoretical background on the different ML algorithms that can be applied for in situ monitoring and control of L-PBF is presented. Then, several examples of the application of ML algorithms for in situ monitoring and control from the literature are reviewed. Finally, the gaps in the literature and future research recommendations are discussed.