## 2 Literature Review
The developed surrogate model was then used to find the uncertainties in the system. A surrogate model provides an alternative and more efficient way to perform uncertainty and optimization analysis. Surrogate modeling of the AM process using machine learning (ML) methods is still in its early stage. ML models can be classified into three types: (1) supervised learning, (2) unsupervised learning, and (3) reinforcement learning. In supervised learning, each input parameter space is labeled with an output, whereas in unsupervised learning, the input parameters do not have an output [16]. On the other hand, reinforcement learning maximizes the reward signals by continuously learning the relation between the situation and the actions. The best example is self-driving cars. Different types of ML models have been used for AM analysis, as shown in Figure 1. For example, a regression approach has been used to predict the AM part quality of parameter optimization, whereas the classification approach can detect defects, quality assessment or prediction, etc. [16; 17]. [PERSON] et al. [18] presented a prediction model for SLM using an ANN method. The ANN model was trained with experimental results and was used to predict the SLM outputs on a set of different parameters. The primary input parameters were the laser power, hatch spacing, and scanning speed. In similar studies [19; 20], an ANN model was used to construct a surrogate map between the laser parameters and outputs such as temperature, strains, etc., for an AM build part. Recently, surrogate models have gained popularity for UA in the AM process. The most commonly used surrogate models in AM are support vector machines (SVM), neural networks (NNs), Gaussian process (GA) models, and polynomial chaos expansion [6]. [PERSON] and [PERSON] [21] developed a methodology to study the uncertainty in a final product's microstructure via the construction process parameters and environmental changes. They replaced the finite element model with the Gaussian surrogate model and applied it to study the relationship between the cooling rate and the final product's microstructure. A similar methodology was adopted by [PERSON] [22], who used a surrogate model to quantify the uncertainties in fatigue crack growth in AM. [PERSON] et al. [23] proposed a Gaussian process-based surrogate model to make a response surface between the SLM process output and the input parameters. Their surrogate model was constructed using 96-single-track experimental data to predict the melt pool depth. The model was then used to calculate the melt pool depth at unobserved input process parameters. Highly efficient machine learning models have provided an alternative framework for optimizing the AM process [17]. [PERSON] et al. [24] used an NN framework to optimize the selective laser sintering process' parameters by minimizing the shrinkage. They trained the NN with experimental data, and then combined it with the genetic algorithm (GA) to find the input parameters' optimal values. The proposed structure can be used to optimize any nonlinear and multitudinous system. [PERSON] et al. [25] used this approach to predict the porosity of an SLM-built part using a Gaussian process-based model. Additionally, they provided a Bayesian framework to estimate the statistical model's parameters, and then used the Kriging method to predict the given parameter settings' porosity. This study's primary disadvantage was that they only two parameters were used as input variables, whereas the SLM can be affected by many factors, including the materials and environment. NNs and GAs are the most widely used methods for the optimization, modeling, and prediction of process parameters. Some of the popular GA methods used in AM are multi-gene genetic programming (MGGP), the multi-objective particle swarm optimizer (MOPSO), the non-dominated genetic algorithm, etc. [26]. [PERSON] et al.