# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Melt Pool Defect Modeling
#### 3.1.1 Thermo-Mechanical Simulations of LPBF Defects Using FEM  
The FEM has been widely used to model the melt pool, being capable of performing indirect sequentially coupled thermo-mechanical analyses of the melt pool's hydrodynamics, heat transfer, and thermal stress behaviors. A significant advantage of the FEM is its ability to capture very minute details of the LPBF process, such as pore formation (LOF, gas porosities, and keyhole porosities), denonudation, melt pool hydrodynamics, and residual thermal stresses. The ability of FEM to capture very fine details of the LPBF process can be attributed to its computational domain being continuous for both powder and gas materials. For LPBF modeling using the FEM, the powder bed is considered continuous with a prescribed porosity, instead of modeling the size and shape of each powder grain in the computational domain. An example of an FEM analysis of LPBF melt pool behavior is provided in Figure 4, which numerically shows the predictability of LOF porosity defect detection in a theoretical multi-track LPBF print. Table 1 lists the thermo-mechanical FEM analyses of the melt pool's behavior and defect generation. The development of overhanging and floating layers produced during the SLM process for single SS316L layers using ANSYS was investigated [26]. Due to the absence of underlying solid material during the print, the melted layer was expected to deform, resulting in shrinkage and cracks. The temperature field and stress distribution extracted from the numerical analysis indicate transverse cracking due to high tensile stresses in the scan direction. The dynamic melt flow effects of an SS316L melt pool on pore defect generation, material sparking, and denudation were analyzed using the 3D powder-scale model ALE3D [27]. Dominant evaporative cooling was shown to affect the recoil pressure substantially. The consequential recoil force overcame the surface tension, creating depression zones and material spatter. After the material cools down to lower than the pool boiling temperature, the liquid surface tension property causes pores to form. The transition region, where the strong Marangoni effect cools the depression zone, creates a denonudation zone that pulls in adjacent particles and creates side pores close to partially melted particles. A new simulation SLM scheme in Deal-II for SS316L was proposed, which implements a new Gaussian line heat source and hybrid heat source to accelerate the simulation process [28]. The temperature field in the melt pool under different heat sources was considered to exhibit high-temperature gradients. It was comparable to experimental efforts, suggesting potential high levels of induced stresses distorting the deposited layers. The pore formation mechanisms and liquid-solid interface dynamics occurring in molten liquid during the LPBF printing of SS316L were investigated [29]. The validated simulation work results indicate that pores form during the change in laser scan velocity due to the rapid formation and subsequent collapse of deep keyhole depressions in the surface, which trap inert shielding gas in the solidifying melt pool. A 3D heat transfer model was presented with adaptive meshing to simulate the multiple-track builds in LPBF for SS316L using ANSYS [30]. The generation of LOF defects was observed at the highest scanning velocity. It was attributed to the melt pool profile being too shallow to overlap with previous deposited layers and tracks. Track discontinuities were also observed and were formed by unstable melt pool behaviors. The thermal and hydrodynamic flow of full- and half-strut melt pools was modeled using ALE3D to estimate the average temperature in situ during powder fusion [31]. A study was performed that coupled a thermo-mechanical simulation for the LPBF process with Ti6 Al4V powder using the FEM code MSC Marc [32]. As more tracks accumulated in the computational domain, a progressive build-up of transverse stress was created.