# 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 Thermal Sensors  
The thermal characteristics of LPBF scans collected from thermal sensors can highlight the formation of defects and suggest the overall part quality of the build. The thermal data collected from IR cameras and pyrometers have been utilized to train ML algorithms to recognize in situ undesired anomalies such as defects. Infrared thermal or thermographic cameras use infrared (IR) radiation to create images. During the LPBF printing process, IR cameras can capture detailed thermal characteristics of the current laser scan, revealing temperature maldistributions that can affect the subsequent scan. Figure 13 displays how an IR camera can highlight the position and geometry of various defects and undesired anomalies. The IR cameras integrated with ML algorithms for detecting defects reported in the literature include forward-looking infrared (FLIR) and short-wave infrared imaging (SWIR) cameras. Pyrometers are remote sensing thermometers that can measure the amount of thermal radiation emitted from the probed surface. Pyrometers integrated with ML algorithms for detecting defects reported in the literature include single-cameratwo-wavelength imaging pyrometers (STWIPs) and dual-wavelength imaging pyrometers. A compiled list of thermal sensors integrated with ML methods for detecting LPBF defects is provided in Table 11.  
A semi-supervised ML algorithm based on the GMM was introduced to investigate balling and overheating phenomena in IN718 LPBF-printed parts [193]. Photodiode sensors extracted thermal radiation and plasma emissions from the melt pool. A CNN model was utilized to analyze thermographic off-axis imaging to detect LPBF printing defects [195]. Delamination and splatter defects were recognized using the proposed model. The model correlated the thermal history and subsurface porosity of Ti6 Al4V LPBF-printed parts under various print conditions [194]. A high-speed infrared camera was used to obtain non-normalized surface temperatures, and a synchrotron x-ray imaging system was used to obtain porosity formation information. Several different ML algorithms were trained to recognize the formation of defects during the printing process, including LR, RF, GBC, and GPC. The micropore distributions in the SS304 LPBF-printed part were predicted using in situ thermographic data and ML [196]. A short-wave infrared (SWIR) imaging camera was used to monitor the spatial and temporal thermal features. Various supervised ML models, including K-nearest neighbor (KNN), random forest (RF), decision tree (DT), multi-layer perceptron (MLP), logistic regression (LR), and AdaBoost models, were trained to detect micropores [197]. A dual-wavelength imaging pyrometer was developed to train various classifier models to detect LPBF defects. The classifier models included KNN, SVM, LR, and CNN. A photodiode sensing system and a pyrometer generated the training data. A CNN-based ML method was employed to retrieve the spatial distribution of melt pool signatures within the coordinate system of the corresponding part [152]. Additionally, by mapping the STWIP-measured melt pool signatures to the registered melt pool coordinates, the melt pool signature maps were reconstructed. The in-process surface topography information generated from the melt pool signature maps can aid in characterizing and detecting defects. An online continuous flaw detection method was presented that combines photodiode signals and melt pool temperature for LPBF-generated products. A set of camera images recorded at a high resolution and speed were used to characterize the molten zone [199]. A correlation connecting the photodiode signal and average melt pool temperature was made using three deep learning algorithms, namely back propagation neural network (BPNN), stacked sparse autoencoder (SSAE), and long short-term memory (LSTM) algo