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

Abstract

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 µm, scanning patter angle of 35°–45° and heat treatment temperature of 638–640°C.

Originality/value

Suitable selection of process parameters and postprocessing treatments lead to a significant reduction in time and cost.

Citation

Panahizadeh, V., Ghasemi, A.H., Dadgar Asl, Y. and Davoudi, M. (2022), "Optimization of LB-PBF process parameters to achieve best relative density and surface roughness for Ti6Al4V samples: using NSGA-II algorithm", Rapid Prototyping Journal, Vol. 28 No. 9, pp. 1821-1833. https://doi.org/10.1108/RPJ-09-2021-0238