Optimization of LB-PBF process parameters to achieve best relative density and surface roughness for Ti6Al4V samples: using NSGA-II algorithm

Purpose This paper aims to study multiobjective genetic algorithm ability in determining the process parameter and postprocess condition that leads to maximum relative density (RD) and minimum surface roughness (Ra) simultaneously in the case of a Ti6Al4V sample process by laser beam powder bed fusion. Design/methodology/approach In this research, the nondominated sorting genetic algorithm II is used to achieve situations that correspond to the highest RD and the lowest Ra together. Findings The results show that several situations cause achieving the best RD and optimum Ra. According to the Pareto frontal diagram, there are several choices in a close neighborhood, so that the best setup conditions found to be 102-105 watt for laser power followed by scanning speed of 623-630 mm/s, hatch space of 76-73 mu m, scanning patter angle of 35 degrees-45 degrees and heat treatment temperature of 638-640 degrees C. Originality/value Suitable selection of process parameters and postprocessing treatments lead to a significant reduction in time and cost.

Automated summary

In this study, the multiobjective genetic algorithm was used to optimize the process parameters and postprocessing conditions for laser beam powder bed fusion of a titanium alloy sample. The results show that by selecting suitable process parameters and postprocessing treatments, significant reductions in time and cost can be achieved.