Optimization of Selective Laser Sintering/Melting Operations by Using a Virus-Evolutionary Genetic Algorithm

This work presents the multi-objective optimization results of three experimental cases involving laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these cases, first one is formulated as single-objective problem aimed at maximizing density Ti6Al4V specimens, with layer thickness, linear energy density, hatching space scanning strategy independent process parameters. The second refers to formulation two-objective both hardness tensile strength samples, power, speed, hatch spacing, scan pattern angle heat treatment temperature Finally, third case deals three-objective minimizing mean surface roughness, while laser-melted L316 stainless steel powder. proposed algorithm are statistically compared those Greywolf (GWO), Multi-verse (MVO), Antlion (ALO), dragonfly (DA) algorithms. Algorithm-specific parameters for all algorithms including virus-evolutionary were examined performing systematic response experiments find beneficial settings perform comparisons under equal terms. have shown that superior heuristics tested, least on basis evaluating regression models fitness functions.