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

다국어 초록 (Multilingual Abstract)

Manufacturing high-quality and desired products from additive manufacturing necessitate careful adjustment of the processparameters. Various methods can be utilised to determine optimum process parameters, such as the Taguchi method, Designof Experiments (DoE). Rather than evaluating limited information obtained from statistical analysis of the experiments, optimisationmethods can help find the best possible combination for the process parameters. Therefore, an optimisation approachbased on Particle Swarm Optimization (PSO) was utilised to find the optimum process parameters. The most importantprocess parameters of Selective Laser Melting (SLM) such as laser power, layer thickness, scan speed, and build orientationwere selected as input parameters, and their effects on the tensile properties of the manufactured part were investigated to findout the optimal operating conditions for the SLM process. Since there is not any explicit mathematical expression relatingthese process parameters to the tensile strength, the Response Surface Method (RSM) was used to obtain a meta-model sothat it can be used as an objective function in the optimisation formulation. This approach enabled us to predict the optimumprocess parameters to maximise the tensile strength without conducting an excessive number of experiments. Moreover, themathematical model can also predict tensile strength corresponding to the parameter values that are not tested according tothe 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.