# 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
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Sensor Type** & **ML Technique(s)** & **Defects Observed** \\ \hline SS GP1 [172] & CT, DSLR camera & SVM & Porosity, entrapped gas pores, elongated voids, LOF \\ \hline IN718 [173; 174; 118] & Visible-light camera, high-speed camera, CMOSIS-sensor camera & CNN, BoW, CNN & Recoater streaking, debris, super-elevation, incomplete spreading, porosities, balling, LOF, surrying, upraising areas/scan-lines \\ \hline Ti6 Al4V [175; 176; 119; 177] & Visual camera imaging, XCT, SEM & Bayesian, CNN, RF & Porosity, defective layers, inconsistent thickness, warping, cracking, porosities, cracks, surface roughness, LOF \\ \hline SS316L [178; 179; 180; 181; 182; 183; 184] & Scanning confocal microscope, visual light camera, XCT, digital single lens reflex camera, 2D optical microscopy, high-speed camera & Kriging-WOA, XGB, LR, SVM, CART, RF, XGB, MLP, KNN, LDA, CNN, BPNN, SCM, DBN, SVM, MLP, KNN, RF, SNS-entrapped pores \\ \hline Al6061T6 [185] & Microstructure imagery & CNN & Porosities, voids, gas entrapment, LOF \\ \hline IN625 [186] & XCT & CAE & Porosity, keyholing, LOF, over melting \\ \hline AlSi10 Mg [187] & XCT & CVAE, CNN & Pores, cracks, LOF, keyhole, discontinuities in a scan path \\ \hline CoCr [8] & Photodiode and CMOS camera & Bayesian & LOF, boiling porosity, cracks \\ \hline Ti6 Al4V [188] & XCT, high-resolution layerwise imagery & NN, CNN & Gas porosity, keyhole pores, LOF \\ \hline Steel [189] & Microstructure generator & CNN & LOF \\ \hline AlSi10 Mg [190] & SEM & GMM, ANN, SVR, RF & Gas-entrapped pores, LOF, keyholes \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A compiled list of optical sensors integrated with ML methods for detecting LPBF defects. A DSLR camera and XCT were utilized to capture the surface and microstructure characteristics of SS-GP1 LPBF-printed parts [172]. A linear support vector machine (SVM) detected defects such as incomplete fusions, porosities, cracks, or inclusions. A pre-trained CNN was used to detect in situ processing defects in LPBF autonomously printed parts [173]. Powder bed images were captured using a visible-light camera. An online monitoring system was developed to evaluate the quality of fusion and defect formation in every layer of Ti6 Al4V LPBF-printed parts using visual camera imaging as the training input [175]. A Bayesian interface classifier was chosen to detect defective layers or regions automatically from the camera imaging. A digitally simulated CT scan with internal micropores was tested to allow for supervised ML training using a random forest (RF) model for LPBF-printed parts [119]. Similarly, a CNN-based online fault recognition system was developed using microstructure images to predict the defects due to the process in IN718 LPBF-printed parts [118]. Observed defects included internal pores and balling. A deep-learning CNN using XCT of Ti6 Al4V LPBF-printed thin-wall fins was trained to recognize defects such as inconsistent thickness, warping, and cracking [176]. A visible-light high-speed camera was utilized to study the IN718 LPBF melt pool morphology [191]. The unsupervised ML method, the bag of words (BoW) method, was used to distinguish differences between the recorded molten pools. A CMOSIS-sensor camera and a CNN were utilized to develop an in-process sensor monitoring system [174]. Researchers quantitatively extracted the microstructural features of a Ti6 Al4V LPBF-printed part from SEM imaging using the random forest (RF) algorithm [177]. A DNN-based classification model for SUS316L was used for melting pool images in LPBF-printed parts [192]. A machine learning method was developed to build optimal LPBF process parameters for satisfactory surface roughness and dimensional accuracy [178]. The Kriging (Gaussian regression) model and whale optimization algorithm (WOA) were used in the data-driven framework to achieve optimal process parameters.