# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Integrated ML Techniques and Optical Sensors  
In situ layerwise imagery of the LPBF printing process is captured through many sensor types, such as cameras, microscopes, photodiode sensors, and XCT techniques. High-speed cameras such as visual-light cameras, digital single-lens reflex (DSLR) cameras, built-in powder bed cameras, and CMOSIS-sensor cameras capture in situ layerwise images of the printed part. Similarly, microscopes such as scanning electron microscopes (SEMs), confocal laser scanning microscopes (CLSMs), and optical microscopes (OMs) also capture layerwise images of scanned surfaces. Captured microscopic imagery has been shown to capture much more robust microstructure characteristics of the part surface than cameras. \begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline
**Defect Type** & **Defect** & \multicolumn{6}{c}{**Sensing Technologies**} \\ \hline Surface quality defects & Balling [151] & - & IRTC & - & - & - & - \\ Surface oxidation & Surface oxidation [74; 130] & - & - & SWIR & STWIP & - & - \\ Surf. roughness [152] & - & - & - & STWIP & - & - \\ Denudation [153] & TIC & - & - & - & - & - \\ Vaporization [153] & TIC & - & - & - & - & - \\ \hline Microstructure defects & LOF porosities [154; 155] & - & - & SWIR & STWIP & - & - \\ Keyhole porosities [154; 156; 157] & - & IRTC & SWIR & - & IPS & - \\ \hline Mechanical defects & Sol. cracking [158] & - & - & - & - & - & DSC \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Thermal-based sensing technologies applicable for detecting LPBF defects. \begin{table}
\begin{tabular}{c l c c c c c c c c} \hline \hline
**Defect Type** & **Defect** & \multicolumn{6}{c}{**Sensing Technologies**} \\ \hline Surface quality defects & Balling [159; 160] & AES & - & NRS & - & - & - & - & - \\ \hline Microstructure defects & LOF porosities [161; 162; 163; 164; 165] & - & FBG & NRS & IU & LU & PU & RUS & PAUT \\ Gas porosities [163; 166] & - & - & - & IU & LU & - & - & - \\ Keyhole porosities [167] & - & - & - & IU & - & - & - & - \\ \hline \multirow{3}{*}{Mechanical defects} & Liq. cracking [168] & AES & - & - & - & - & - & - & - \\  & Sol. cracking [167; 169; 170] & AES & - & - & IU & LU & - & - & - \\ \cline{1-1}  & Delamination [167; 171] & - & - & - & IU & LU & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Acoustic- and ultrasonic-based sensing technologies applicable for detecting LPBF defects. Photodiode sensors convert light emissions to an electrical voltage or photocurrent. Frequently, photodiode sensors are paired with optical cameras to predict defective locations based on the light emission signals. X-ray computed tomography (XCT) instruments direct narrow X-ray beams at an object to produce cross-sectional images, which can be stacked together to create a 3D image of the object. Optical sensor data are the most popular data used to train ML algorithms to recognize and classify LPBF defects. To train ML algorithms, layerwise imagery of the LPBF printing process is collected and processed based on chosen parameters. Figure 12 presents a process schematic detailing the implementation of CT scan data into SL learning. Often, surface characteristics investigating LPBF are used to classify the part quality. For example, the layerwise image of a particular scan can be categorized into poor, medium, and high quality, specifically regarding porosity. LPBF-printed part layerwise imagery post-processing characteristics can be correlated with appearance classifications, such as surface roughness, surface morphology, and stress characteristics. Defect characteristics such as size, shape, and location can also be used to classify the quality of the LPBF-printed part. A compiled list of optical sensors integrated with ML methods for detecting LPBF defects is provided in Table 10.