# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 1 Introduction  
Additive manufacturing (AM) is a sequential process that consecutively deposits layers to generate designed geometries of complex parts. The applicative range of AM has been proven to be incredibly vast and is continuing to receive increased attention for its rapid and precise prototyping in many industries. Generally, AM processes offer more freedom in part design with much shorter lead times than classical metal part fabrication technologies. In metallic AM processes, a powder or wire feedstock is fully melted using a beam (laser or electron) energy source to lay the present layer in a specific configuration and the process is repeated to complete the entire part. AM can be classified according to the binding mechanism of consecutive layers in a material. The primary binding mechanisms are electron beam melting (EBM), laser beam melting (LBM), and laser beam metal deposition (LMD). Other commercial technologies include laser powder bed fusion (LPBF), selective laser melting (SLM), direct metal laser sintering (DMLS), and laser metal fusion, all of which are under the LBM category. Though all of the mentioned binding mechanisms have been successfully demonstrated, the LPBF process has been specially vetted and shown to be one of the most dependable binding mechanisms [1].  
In LPBF, a layer of metallic powder is dropped by a hopper, distributed, and smoothed over the platform using a leveling system. A direct laser beam from a reflective mirror scans individual tracks to melt the metallic particles. Following the completion of the construction of the current layer, the platform is slightly lowered to provide space for the next layer to be built. The process is repeated to complete the part. Most existing LPBF technologies utilize laser beams with an intensity of a few hundred watts and a beam diameter of up to 100 \(\upmu\)m [2]. Additional process parameters mainly depend on themachine's capabilities and limitations, including the beam velocity, the individual metallic particles' thickness, and the distance between laser scan tracks.  
The LPBF process has shown a high accuracy in generating complex metal parts. However, these are not immune to defect formations. The major sources of LPBF metal part defects are the properties of the metal powder (thermophysical and chemical) and the printing conditions (energy, speed, and inert gas conditions). Theoretically, a metal part with minimal or almost no defects can be produced by integrating optimized process conditions and appropriate metal powder properties [1; 2]. The conditions of a successful defect-free single build may not be the optimum choice for another, and unexpected defects that were not recognized in previously printed parts can be produced. Extensive efforts have been reported investigating the material properties and process parameters that may trigger LPBF defects. The majority of research activities have focused on three primary sources: applying in situ or post-print sensing technologies (thermal, acoustic, optical, and ultrasonic), numerical modeling (finite volume and finite element methods), and implementing different types of artificial intelligence (AI) algorithms (unsupervised, supervised, and semi-supervised) [1; 2; 3].  
This paper summarizes many recent publications on the roles of numerical modeling and the integration of AI with sensing technology for LPBF defect detection. It informs readers of recently applied approaches and technologies to the detection of defects in LPBF-printed metal parts.