Determining the Optimum Process Parameters of Selective Laser Melting Via Particle Swarm Optimization Based on the Response Surface Method

Abstract

Manufacturing high-quality and desired products from additive manufacturing necessitate careful adjustment of the process parameters. Various methods can be utilised to determine optimum process parameters, such as the Taguchi method, Design of Experiments (DoE). Rather than evaluating limited information obtained from statistical analysis of the experiments, optimisation methods can help find the best possible combination for the process parameters. Therefore, an optimisation approach based on Particle Swarm Optimization (PSO) was utilised to find the optimum process parameters. The most important process parameters of Selective Laser Melting (SLM) such as laser power, layer thickness, scan speed, and build orientation were selected as input parameters, and their effects on the tensile properties of the manufactured part were investigated to find out the optimal operating conditions for the SLM process. Since there is not any explicit mathematical expression relating these process parameters to the tensile strength, the Response Surface Method (RSM) was used to obtain a meta-model so that it can be used as an objective function in the optimisation formulation. This approach enabled us to predict the optimum process parameters to maximise the tensile strength without conducting an excessive number of experiments. Moreover, the mathematical model can also predict tensile strength corresponding to the parameter values that are not tested according to the DoE chosen for such studies. Furthermore, it was also shown that the PSO outperforms the Genetic Algorithm (GA), which is widely employed to find out the optimum process parameters, in terms of less number of iteration.