# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 2 Machine Learning
### Supervised Learning  
Supervise learning requires a pre-collected dataset to train predictive ML models, with the data labeled with output variables. It can be categorized into two types: regression and classification, depending on the nature of the output targets. Various ML models are available for supervised learning. For regression problems, options include linear and polynomial regression, while logistic regression is commonly used for classification tasks. Other methods, such as SVMs, multi-layer perceptron (MLP), decision trees, and k-nearest neighbors, have variations tailored for handling both regression and classification tasks [69]. These ML algorithms are often referred to as \"shallow\" because they typically involve only one layer of non-linear transformation to map input features to outputs. For instance, [PERSON] et al. proposed a framework for constructing a process map for AM [61]. They integrated a decision function, representing the porosity density of parts fabricated by AM, into SVM to predict process conditions (good or bad) based on the observations from the parts' surface. This method was shown to be effective in customizing optimized process conditions with a reduced number of experiments. However, shallow ML algorithms generally have a limited capacity to capture complex patterns in data and are often used for simple tasks. In contrast, DL models or neural networks with multiple layers of transformations on input features become necessary when dealing with large or complex datasets [70]. One commonly used DL model is the artificial neural network (ANN) or fully connected neural network, which comprises an input layer, one or more hidden layers, and an output layer, as shown in Figure 1. The input data are fed into the input layer, where each node represents an input feature. Subsequently, the data passes through the hidden layers successively. Each layer applies weighted connections and activation functions to transform the input data into a more abstract representation. Notably, each neuron in a hidden layer is connected to all neurons in the previous and subsequent layers, rendering the neural network fully connected. Commonly used activation functions include the sigmoid function, rectified linear unit (ReLU), radial basis function (RBF), and the tangent hyperbolic function [71; 72]. The final layer of the network is the output layer, which is responsible for predicting the output targets. The number of nodes in this layer depends on the dimension of the output. No activation functions are typically applied in the output layer for regression tasks, whereas the sigmoid function is used for binary classifications and the softmax function for multiclass classifications. Discrepancies between predictions based on the aforementioned feedforward process and actual outputs result in a so-called loss function. Network training aims to determine proper neuron weights to minimize this loss function. This optimization process involves backpropagation to iteratively update network weights using the gradient descent method or its variations. It is worth mentioning that physics-informed neural networks have recently garnered attention by incorporating physical laws into neural network training [73]. Consequently, the loss function comprises data loss and physics loss calculated from differential equations. Figure 1: The architecture of an ANN. CNNs constitute a unique class of DL algorithms specifically tailored to process and analyze data with a grid-like topology [31]. Their most prevalent application lies in image processing for computer vision tasks, where images are typically represented as grids of pixels. However, CNNs are versatile and capable of handling various other data types, including videos, two-dimensional grid geological data, three-dimensional volumetric data, and text structured as a one-dimensional grid.