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 the laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these three experimental cases, the first one is formulated as a single-objective optimization problem...

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Bibliographic Details
Published in:Machines (Basel) 2023-01, Vol.11 (1), p.95
Main Authors: Fountas, Nikolaos A., Kechagias, John D., Vaxevanidis, Nikolaos M.
Format: Article
Language:English
Subjects:
additive manufacturing
Chromosomes
Evolutionary algorithms
Genetic algorithms
Hardness
Heat treatment
heuristics
Laser sintering
Maximization
Multiple objective analysis
Optimization
Process parameters
Rapid prototyping
Regression models
Scanning
selective laser sintering
Sintering (powder metallurgy)
Stainless steels
Surface roughness
Tensile strength
Thickness
Titanium base alloys
Viral infections
virus-evolutionary genetic algorithm
Viruses
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Summary:This work presents the multi-objective optimization results of three experimental cases involving the laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these three experimental cases, the first one is formulated as a single-objective optimization problem aimed at maximizing the density of Ti6Al4V specimens, with layer thickness, linear energy density, hatching space and scanning strategy as the independent process parameters. The second one refers to the formulation of a two-objective optimization problem aimed at maximizing both the hardness and tensile strength of Ti6Al4V samples, with laser power, scanning speed, hatch spacing, scan pattern angle and heat treatment temperature as the independent process parameters. Finally, the third case deals with the formulation of a three-objective optimization problem aimed at minimizing mean surface roughness, while maximizing the density and hardness of laser-melted L316 stainless steel powder. The results obtained by the proposed algorithm are statistically compared to those obtained by the Greywolf (GWO), Multi-verse (MVO), Antlion (ALO), and dragonfly (DA) algorithms. Algorithm-specific parameters for all the algorithms including those of the virus-evolutionary genetic algorithm were examined by performing systematic response surface experiments to find the beneficial settings and perform comparisons under equal terms. The results have shown that the virus-evolutionary genetic algorithm is superior to the heuristics that were tested, at least on the basis of evaluating regression models as fitness functions.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines11010095