###### Abstract  
Selective laser melting (SLM) is a metal-based additive manufacturing (AM) technique. Many factors contribute to the output quality of SLM, particularly the machine and material parameters. Analysis of the parameters' effects is critical, but using traditional experimental and numerical simulation can be expensive and time-consuming. This paper provides a framework to analyze the sensitivity and uncertainty in SLM input and output parameters, which can then be used to find the optimum parameters. The proposed data-driven approach combines machine learning algorithms with high-fidelity numerical simulations to study the SLM process more efficiently. We have considered laser speed, hatch spacing, layer thickness, Young modulus, and Poisson ratio as input variables, while the output variables are numerical predicted normal strains in the building part. A surrogate model was constructed with a deep neural network (DNN) or polynomial chaos expansion (PCE) to generate a response surface between the SLM output and the input variables. The surrogate model and the sensitivity analysis found that all five parameters were important in the process. The surrogate model was combined with non-intrusive optimization algorithms such as genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) to perform an inverse analysis and find the optimal parameters for the SLM process. Of the three algorithms, the PSO performed well, and the DNN model was found to be the most efficient surrogate model compared to the PCE. s _Applied_  
27 December 2021  
19 February 2022  
**Problems:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **Copyright**: © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))). **Copyright**: © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. these material parameters and the process parameters may need to be optimized. SLM is a multiphysics process that occurs over different time and length scales [4]. Among the major physical phenomena that occur during the process are radiation, convection phase change, absorption and phase change, etc. [5]. Our understanding of the SLM process can be improved by using the design of experimental methods [5]. However, experimenting on a large scale is time-consuming and costly. Such challenges can be addressed using a realistic numerical model of the process. A numerical model can help us generate a large number of data sets at a significantly lower cost than traditional experimental methods. However, SLM involves complex multiphysics and multiscale phenomena [4] with many uncertainties, from the material powder bed to the melting and solidification processes. These uncertainties come from an insufficient understanding of the process, measurement error, scaling error, sampling error, and lack of information about the material properties [6]. All these elements contribute to the model's confidence level, which may limit SLM applications. To increase the SLM product quality and production, we need to understand the sources of uncertainties; how they grow, and how they affect the final product quality. Two methods can be considered to tackle these issues and to increase trust in the SLM numerical model [7] or the process: (1) uncertainty analysis (UA), and (2) sensitivity analysis (SA).