## 1 Introduction  
Additive manufacturing (AM) is defined in ISO/ASTM 52900-2021 standard [1] as a process that builds parts by joining a material layer by layer, using 3D model data. The standard defined seven process categories for AM, namely binder jetting, directed energy deposition, material extrusion, material jetting, powder bed fusion, sheet lamination, and vat photopolymerization. AM are also referred to as rapid prototyping, 3D printing, layered manufacturing, and digital manufacturing [2]. There are two levels for AM processes. The first one is the digital level where the CAD model is prepared, converted to stl.file, and where machine control strategy is generated (e.g., CAM, G code). The second level is the physical level which contains part manufacturing using one of the seven processes mentioned previously [3]. Initially, AM or 3D printing technologies were used mainly for rapid prototyping, but with improvements in the reliability and efficiency of AM processes, as well as in the material properties of the components produced, they are increasingly used in creating complicated customized parts, visualizing tools in design, and producing serial components with small volume. In the future, it might be used for manufacturing clothes, creating food confections, and human organs [2]. There are many advantages for using AM processes, such as the flexibility in the producible designs, the facilitated customization of products, and the capability to print highly complex structures. However, many challenges and drawbacks remain. More specifically, some disadvantages are the stair-stepping effect that appeared in the manufactured product, void formation between the layers resulting from binding reduction, and mechanical proprieties and microstructure variation [4]. The applications for using AM include prototyping during the product development phase, producing products in pilot series production where injection molding costs are high, and creating complex geometry parts which cannot be produced by other means of manufacturing [5]. This gives the edge to AM among manufacturing processes. For example, it allows the integration of additional functionality in components, such as the production of repeated patterns internally. Thus, AM has the capability for combining internal complex structures with more regular outer geometries, reducing weight while keeping structural and aesthetic integrity, as can be done in the manufacturing of a bike frame. Furthermore, applications are emerging in the context of the highly regulated aerospace and automotive sector. For instance, metal parts suitable for aircraft were directly manufactured using titanium material [4], reducing lead time by 30-70%, non-recurring fabrication costs by 45%, and reducing the manufacturing cost for producing low-volume parts by 30-35%. However, metal-based AM such as Powder Bed Fusion (PBF) still has many issues such as porosity, part deformation, and cracks formations [6] that remain to be tackled. Among them, the presence of porosity is particularly critical, as this can have significant effects on the mechanical properties of components, cause failures for the structure, and may decrease the strength and modulus of Young [7]. Thus, the focus of this paper is to facilitate the study of porosity formation in AM. A common approach in the study and optimization of emerging manufacturing techniques is the use of Artificial Intelligence (AI) technologies. AI facilitate intelligent systems development [8] to maximize the efficiency and productivity of manufacturing machines. AI techniques include Artificial Neural Network (ANN), inspired by the biological human brain. It can be used to investigate the complex relationships in big data [9] and extract meaningful implicit patterns to predict complex systems' future states [10]. Deep Learning (DL) techniques are established as an extension to the capabilities of ANN, as they extract features, automatically producing better learning capability [11].