## 2 Literature Review  
Uncertainty sources can be classified into two categories: epistemic and aleatory uncertainty. Aleatory uncertainty in a system occurs due to natural causes such as fluctuation in the laser power, etc. The sources of epistemic uncertainty, meanwhile, are an incomplete understanding or a lack of knowledge, which can be further divided into two types, data uncertainty and model uncertainty. Data uncertainty arises because of improper measurements and an insufficient amount of measured data. Model uncertainty is used to quantify the difference between experimental data and numerical simulation models. For example, model uncertainty may occur due to the assumptions made to simplify the numerical model [6]. To understand the relationship between uncertainties in input and output parameters, we proposed performing a sensitivity analysis (SA). These can be divided into two types, local and global sensitivity analyses. Local sensitivity analysis is derivative-based; this technique analyzes the effect of one parameter on the process by keeping other parameters constant. In global sensitivity analysis, all the input parameters are varied simultaneously, and the sensitivity is calculated over the whole sample space of each input. There are different approaches to performing sensitivity and uncertainty analysis in SLM. The most popular method is trial and error experimentation. [PERSON] et al. [10] conducted an experimental investigation to determine the influences of input parameters (laser speed, build direction, and layer thickness) on SLM output. They used a full factorial experimental design technique with three factors, and two levels for each factor. The dimensional accuracy, roughness, and mechanical properties were considered to measure the effect of input parameters. UA and SA of the AM process using experiments may give good results to some extent, but these analyses have various disadvantages. For example, experiments on a large scale lead to material wastage and a delay in final product delivery, subsequently increasing the product and process cost. Additionally, one component's results may not be applicable for other SLM products, as results may vary with the problems [6]. However, these issues can be addressed by using an efficient numerical simulation model. [PERSON] et al. [11] used finite element modeling to analyze 2D temperature profiles and melt pool geometry. They performed a sensitivity analysis by changing one input parameter at a time while keeping the other parameters constant, and found that the powder reflectivity affects the melt pool geometry the most. However, changing parameters one at a time does not always provide good results, as this process fails to consider the interaction of parameters. [PERSON] et al. [12] conducted a global sensitivity analysis for SLM using a 2D numerical simulation model. They used 26 input parameters and calculated their influence on melt pool width, length, and maximum temperature. The effect on the output was calculated by simultaneously changing the input parameters instead of changing one at a time. They used the elementary effect technique and found that 16 of the parameters did not significantly affect the process output. Similarly, [PERSON] et al. [13] used a numerical model for single-track simulation to identify uncertainties in the SLM process. A 3D and more complex model for a single-track SLM was used by [PERSON] et al. [14] to identify the critical variable. They used a design of experiment (DOE) technique with a two-level screening system to separate the vital parameters. As the numerical simulation becomes more complex, the computation cost of the simulation will also increase. To make the numerical simulation more efficient, [PERSON] et al. [15] proposed a framework to combine the numerical and experimental approaches to find critical parameters. They initially found the essential parameters and then used them to devolve a surrogate model for the melt pool.