# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Summary of Applicable ML Methods
#### 4.1.1 Supervised Learning (SL)  
_Support Vector Machine (SVM)_: A binary SL classifier that uses ground truth labels to train in detecting anomalies. Layerwise imagery information used for training is extracted by discretizing images into voxels. The relative difference in grayscale intensity values between voxels reported from the layerwise imagery can indicate the local part density and suggest various defects such as LOF voids (a low intensity surrounded by a high intensity) and spatter contaminants (a high intensity surrounded by a low intensity). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Powder Material** & **Defect(s) Observed** & **Code(s)** \\ \hline IN718 [111, 113] & Seeded voids, natural voids, volumetric porosity, surface roughness & ABAQUS \\ \hline Ti6 AI4V [109, 110, 112] & Spherical pores, LOF defects & ABAQUS, ANSYS \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Compiled list of mechanical FEM analyses of embedded LPBF defects. Multilayer Perceptron (MLP): A forward-predicting SL neural network trained iteratively on a dataset while minimizing a loss function [115]. An MLP is considered one of the most complex ML algorithm types for optimization due to the relatively high number of hyperparameters [116]. An MLP trains by giving the network an input vector containing earlier observations that can be used to output the future value of a variable. The output can be considered a posterior probability, and the node with the highest value defines the final classification. Data collected from LPBF prints can be classified based on the training data's print quality (good, better, or best). K-nearest neighbors (KNN): An SL algorithm that relies entirely on input data and does not consider any assumptions or generalizations [117]. The training data are first input into the algorithm to transform the raw data into feature vectors. Then, a parameter that defines the number of the nearest neighbors to be included in most of the voting process, known as \"k,\" is defined. \"k\" is frequently described as N1/2, where N equates to the dataset size used in algorithm training. A KNN model can detect LPBF defects by measuring the Euclidean distance from the sample to its k-nearest neighbors. Logistic Regression (LR): A statistically based SL algorithm that finds the optimal hyperplane to separate examples based on their class label. It is accomplished by estimating an event's probability for a given dataset of independent variables, also known as the logit model. Convolutional Neural Network (CNN): An SL algorithm that consists of three layers, namely convolution (Conv), pooling, and fully connected layers. Regarding LPBF defects, the Conv layers combine input photos or image data with pooling data to create convolution blocks. A group of convolution blocks are used to realize a deep architecture. Patterns can be extracted from the multi-level image features through these groups of convolution blocks, which refine the model's pattern recognition capabilities [118]. Extractable patterns can include defects. Decision Tree (DT): A non-parametric SL method that predicts variables by learning data features' decision rules and is presented as a piecewise constant approximation. Random Forest (RF): An SL algorithm that extracts the training data to create various random DT sets. The multiple decision outcomes generated from RF models are accomplished through bootstrap (Monte Carlo) aggregating, also called \"bagging.\" During the algorithm's execution, the \"baggs\" of decision trees vote for the likely outcome of each instance. The votes ultimately determine the overall classification identity [119]. The RF model is also known as random forest classification (RFC). Classification and Regression Tree (CART): An SL-DT model that picks input variables and evaluates split points on those variables to produce an appropriate tree. Each fork in  
Figure 10: Summary of ML methods applicable for detecting LPBF defects.