Simultaneous Optimization of Surface Roughness and Mechanical Properties of 316L Produced by LB-PBF Using Grey Relational Analysis Complemented by Residual Stress Analysis

Abstract

This study presents the adoption of the grey relational analysis (GRA) method and an objective weighting criterion (CRITIC) to select the best printing parameters (laser power, scan speed, hatch distance, and layer thickness) for achieving the minimum surface roughness. At the same time, the material hardness, ultimate tensile strength, elongation at break, and relative density are maximized and followed by a complementary residual stress assessment of selected samples. A Box Behnken experimental design of 27 experiments with a factor variation at three levels was chosen to characterize the laser-based powder bed fusion process of 316L stainless steel. This study is guided by a multi-criteria decision-making (MCDM) approach to simultaneously consider all the factors that yield the most proper printing parameters. Due to the multi-directionality of the problem analysed, four additional MCDM methods are also employed to avoid any limitations in the solutions. The correlation between these techniques was evaluated through Spearman’s coefficient. Additionally, the reliability of the optimal printing outcomes was experimentally confirmed, improving the final surface quality by 8.17%, the elongation and ultimate tensile strength by 4.62% and 4.35%, respectively. Moreover, the residual stresses obtained were all of compressive nature, and their magnitude is comparable with those already published works.