# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Numerical Simulation Efforts of LPBF Defects
#### 3.2.2 Mechanical Simulations of Statistically Driven LPBF Defects Using FEM  
Reconstructed XCT-scanned geometrical models have been proven to match the actual LPBF-printed part compared to its idealized CAD model. However, the generation of reconstructed CAD models from CT scans and the subsequent FEA of such CAD models are very computationally expensive. The statistical characteristics of the CT scans have been extracted, producing stochastic geometrical defect models that can be applied to lattice struts and can reduce computational costs. Table 5 lists mechanical FEM analyses of statistically driven LPBF defects. A study fitted the distribution of geometric imperfections into a continuous probability density function by a Kernel density estimation [101]. They used the developed script to present a series of datasets of defects feeding the numerical simulation of AISi10 Mg LPBF-printed regular octet and rhombicuboctahedron units. An XCT was utilized to capture the realistic geometrical information of multi-layer lattice sandwich panels [102]. The statistical characteristics of the CT scans were used to develop a novel model that can describe a non-uniform distribution of geometrical imperfections in the analyzed specimen. The investigated geometrical imperfections included a varying strut diameter, irregular strut cross-section, internal cavities, and strut mismatch among hybrid or graded cells. An investigation presented the mechanical responses of an AISi10 Mg LPBF-printed lattice structure with stochastic geometrical defects using the FEM [103]. XCT was utilized to capture the surface morphology and defect distribution within the part. These defects included strut porosity, thickness variation, and waviness. The CT-constructed geometrical defects were implemented into an ideal FEM analysis. The numerical results indicate that the strut thickness variation had the most considerable impact on energy absorption of the lattice structure compared to porosity and strut waviness. \begin{table}
\begin{tabular}{c c c c} \hline
**Powder** & **Defect(s)** & **Model** & **Code(s)** \\
**Material** & **Observed** & **Generation** & **Code(s)** \\ \hline IN625 [94] & Micropoosity, variation in cross-sectional geometry along the strut length, waviness (deviation of strut’s axis across its length) & XCT & Not listed \\ \hline Co28 Cr6 Mo [97] & LOF & XCT & ANSYS \\ \hline AISi12 [86] & Gas pores, technical fatigue cracks, critical cracks & XCT & ABAQUS \\ \hline AISi7 Mg [89] & Crack nucleation and propagation, internal porosity, strut cracks & XCT & ABAQUS \\ \hline \multirow{4}{*}{Ti6 Al4V [88, 91, 92, 95, 98]} & Variable cross-section (CS), strut waviness, irregular protrusions, CS eccentricity, missing and interrupted struts, uneven distribution of material in proximity of junction, sharp edges created from overhanging regions, excess material, geometrical imperfections, under-sizing/oversizing, diameter variation, CS shifts, voids & XCT & ANSYS, ABAQUS \\ \hline SS316L [96, 99, 100] & Increased porosity, voids, inclusions, varying shell thickness, holes, surface waviness, roughness, large internal pores, incomplete melting & XCT & ANSYS, ABAQUS \\ \hline AISi10 Mg [87, 90, 93] & Microcavities, surface roughness, geometrical irregularities, surface roughness, void defects, LOF, large voids caused by unstable melt pools & XCT & ABAQUS \\ \hline \end{tabular}
\end{table} 
Table 4: A compiled list of mechanical FEM analyses of reconstructed CT-scanned geometrical LPBF defects. A similar study performed automated image-based FEA modeling of an LPBF Ti6 Al4V lattice [104]. The geometrical mesh of the lattice was produced using X-ray tomography, and the geometrical defects in the lattice structure were simulated using the FEM. A method was developed to integrate data-driven AM defect modeling, Markov chains, and a Monte Carlo simulation to predict the stiffness of an AM structure [105].