Determination of process parameters for selective laser melting of inconel 718 alloy through evolutionary multi-objective optimization

Selective laser melting (SLM) is a sustainable process that offers various environmental benefits. However, the parts produced from SLM process require post-processing treatments that increase the energy consumption. Therefore, there is a need for optimization of SLM input parameters to minimize the same. For this purpose, the data set on selectively laser-melted Inconel 718 parts was obtained from the reference. An evolutionary neural net has been employed to model the objective functions: specific energy consumption, relative density and surface roughness in the present study. The neural net strategy was successful in capturing the important trend of the three objectives by achieving a maximum correlation coefficient of 85% in each of them. Subsequently, the trained model is used in tri-objective optimization to yield the optimum input parameters. A close agreement is observed between the predicted optimum parameters and experimentally obtained parameters, proving the formulated strategy to be reliable and effective.