# 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
#### 3.1.2 Thermo-Mechanical Simulations of LPBF Defects Using FVM
\begin{table}
\begin{tabular}{c c c c} \hline
**Powder** & **Defect(s)** & **Laser Energy Rate (W),** & \\
**Material** & **Observed** & **Velocity (mm/s), and** & **Code(s)** \\  & & **Diameter (mm)** & \\ \hline SS316L & Gas bubbles, balling, interlayer porosities, surface & & OpenFOAM, \\
[55; 56; 57] & roughness, melt pool depression, interlayer & 50–400, 400–6000, 54–100 & FLOW3D, ANSYS \\  & overlap, spatter ejection & & Fluent \\ \hline AlSi10 Mg [56; 64] & Spatter ejection, large laser penetration depth, high surface tension & 150–200, 100–1000, 70 & ANSYS Fluent \\ \hline  & Intertrack voids, gas bubbles, LOF, deep keyhole, depressions, absorptivity fluctuation, recoil & & \\ Ti6 Al4V & pressure-induced vapor depression instabilities, melt pool instabilities, surface roughness, pinched-off voids, high surface roughness, vapor recoil/shear, near-spherical pores, irregular pores, large residual stresses & 60–360, 300–3000, 60–240 & OpenFOAM, \\  & & & FLOW3D, FLOW3D \\  & & & WELD, \\  & & & ANSYS Fluent \\ \hline  & Large laser penetration depth, surface tension, individual droplets, incomplete spreading, keyhole pores, solidification front, protrusions in depression zones & 150–416, 100–400, 70–90 & ANSYS Fluent, FLOW3D \\ \hline Al6061 [73] & Keyhole pores, solidification front, protrusions in depression zone & 416, 200, 90 & FLOW3D \\ \hline AlSi10 Mg & Pore formation, gas bubbles, high surface roughness, lack of bonding, gas bubbles, gas pores & 60–340, 1000–1300, 150 & ANSYS Fluent \\ \hline IN718 [67; 68; 69] & Keyhole, melt pool depressions, gaps & 175–315, 800–960, 50–100 & FLOW-3D, ANSYS Fluent \\ \hline Cu10 Sn [68] & Gas porosities, intertrack fusion & 175, 800, 80 & ANSYS Fluent \\ \hline
17-4 PH SS [70] & Overhang regions, adhesion of powder clusters, warp deformation, dorsal formation & 70–130, 700–1300, 70 & FLOW-3D/FLOW-Weld \\ \hline Invar36, Cu10 Sn, Invar36/Cu10 Sn [71] & Irregularly shaped powder beads, partially melted powder, unmelted powder, pores, keyhole, balling & 125–250, 150, 80 & ANSYS Fluent \\ \hline AISI H13 Steel [72] & Keyhole formation & 120–250, 1000–1500, 52 & FLOW-3D/FLOW-Weld \\ \hline  & Pore-induced microcracks, interconnected micropores, solidification microcracks and micropores, microfocusaves, microgrooves, unsteady keyhole motion & 100–250, 100–500, 85–128 & TATM-MEX \\ \hline GH4169 [75] & Balling, spherical pores, ellipsoidal pores & 130–175, 1500, 54 & OpenFOAM \\ \hline Cu-7 Sn [76] & Balling & 50–200, -, - & OpenFOAM \\ \hline \end{tabular}
\end{table} 
Table 2: Compiled list of thermo-mechanical FVM analyses of the melt pool behavior and defect generation. 3.1.3. Thermo-Mechanical Simulations of LPBF Defects Using Analytical Modeling  
A reduced-order computational model was developed to quickly guide the printing parameters, including laser beam energy and velocity, powder size, and thickness [79]. The investigated powder material was SS316L. A coupled finite difference framework was utilized to simulate the heat transfer from the metallic powder to the underlying substrate. The particles were modeled as thermally and mechanically interacting discrete elements, capturing inhomogeneities in the powder layer. As a result of the rapid cooling and solidification rates simulated, vaporized particles can form gas bubbles in the solidification part, which can decrease the part density. Also, the ablated particles could increase the printed layer's surface roughness and cause inhomogeneities in layer thickness for subsequent layer depositions. A presented thermomechanical model was developed to numerically predict the geometry of the Ti6 Al4V LPBF build and determine the specimen's induced stress using an island scan approach [80]. The model is designed to capture the deformation dependence and heat treatment annealing at elevated temperatures. A physics-based analytical model in MATLAB was developed to predict the part porosity in the LPBF of Ti6 Al4V with given process parameters, material properties, and powder size distributions [81].