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

被引:22
作者
Fountas, Nikolaos A. [1 ]
Kechagias, John D. [2 ]
Vaxevanidis, Nikolaos M. [1 ]
机构
[1] Sch Pedag & Technol Educ ASPETE, Dept Mech Engn, GR-15122 Amarousion, Greece
[2] Univ Thessaly, Dept FWSD, Design & Mfg Lab DML, Kardhitsa 43100, Greece
关键词
virus-evolutionary genetic algorithm; optimization; selective laser sintering; heuristics; additive manufacturing; hardness; density; tensile strength; MULTIOBJECTIVE OPTIMIZATION; PARAMETER OPTIMIZATION; STRENGTH; QUALITY;
D O I
10.3390/machines11010095
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页数:21
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