# 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
The experimental data included surface roughness measurements taken using a surface roughness tester and surface topography measurements taken using a laser scanning confocal microscope. Sixteen groups of experiments were used to train the Kriging model. A synthetic and XCT dataset was used to train a conditional variational autoencoder (CVAE) and a CNN to predict pore occurrences  
Figure 12: Process schematic example of implementing CT scan data into supervised machine learning [172]. in AlSi10 Mg LPBF-printed parts [187]. A framework to process XCT-generated melt pool images of IN625 LPBF-printed parts was presented using a convolutional autoencoder (CAE) neural network [186]. An in-process fault detection and part quality prediction method for LPBF-printed parts was developed using a Bayesian classifier [8]. An XGBoost (XGB) model was trained to predict localized defects for SS316 LPBF-printed parts using defect data from in situ images of LPBF-printed parts as the training data [179]. The root causes of SS316 LPBF-printed part quality were analyzed using various ML classifier models, including a LR, SVM, CART, RF, XGB, MLP, KNN, and LDA [180]. A CNN-based image colorization algorithm was presented to predict tensile plane strain field components of Al6061T6 LPBF-printed part microstructures featuring porosity defects [185]. XCT image data were utilized to train neural networks (NNs) and CNNs to predict defects in images of Ti6 Al4V LPBF-printed build layers [188]. A deep transfer learning (DTL) model was developed to combine transfer learning and deep CNN to perform an in situ quality inspection of LPBF-printed parts using XCT-scanned layerwise camera images [181]. The proposed DTL method successfully monitored part quality, which can help reduce porosity defects during the printing process. It was utilized to build a large dataset of stress responses of 100,000 random microstructure images to train a modified U-Net style CNN model that can predict the elastic stress fields in images of LPBF-printed parts containing defects [189]. Experiments were performed by applying the model to real LPBF microstructures with severe LOF defects. The proposed CNN model could accurately predict the structural response of LPBF microstructures containing defects. Using two high-speed imaging cameras for monitoring, the temperature profile of an SS316 LPBF was reported. The validity of several different detection classifier models was evaluated to detect various defects, including SVM, MLP, KNN, RF, and CNN models [182]. An innovative image and data processing approach was developed to extract high-accuracy level melt pool data [183]. A 2D optical microscope was used to monitor the temperature of molten pool images in situ for multitrack and multilayer printing processes. A series of models, including back propagation NN (BPNN), support vector machine (SVM), and deep belief network (DBN) models, were developed to align the feature data with porosity modes using 2D optical micrographic observations [183]. Three semi-supervised ML approaches were used to detect anomalies and defects in SS316L LPBF builds using Vgg16, ResNet50, and Xception [184]. These three approaches are CNN-based algorithms. The training image data were generated using a digital single-lens reflex camera. The influence of layer thickness, stress ratio, and defect properties of AlSi10 Mg LPBF-printed parts on the fatigue life was investigated [190]. SEM imaging provided microstructure morphology and tractography data and was used to train three ML models. These models are the artificial neural network (ANN), the support vector regression (SVR), and the random forest (RF) algorithms. The RF model best predicted the fatigue life of the LPBF-printed AlSi10 Mg samples.