Open AccessData-driven multi-objective optimization of laser welding parameters of 6061-T6 aluminum alloy
Author(s) -
Jianzhao Wu,
Shuaikun Zhang,
Jiahao Sun,
Chaoyong Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1885/4/042007
Subject(s) - metamodeling , sorting , kriging , latin hypercube sampling , welding , genetic algorithm , multi objective optimization , process (computing) , sampling (signal processing) , design of experiments , computer science , materials science , mechanical engineering , mathematical optimization , algorithm , engineering , mathematics , monte carlo method , statistics , machine learning , filter (signal processing) , computer vision , programming language , operating system
In this paper, a data-driven multi-objective optimization approach using optimal Latin hypercube sampling (OLHS), Kriging (KRG) metamodel and the non-dominated sorting genetic algorithm II (NSGA-II) is presented for the laser welding process parameters on 6061-T6 aluminum alloy. The experiments are designed by OLHS and carried out to obtain the data results. The complex relationship between the process parameters and the bead profile geometry is established by KRG using the data results. The accuracy of the established KRG metamodel is validated using experiments. Then, NSGA-II is used to explore the design space and search the Pareto optimal solutions of process parameters. Besides, the validation experiments were carried out to obtain ideal LW bead profile, which shows that the approach can bring dependable guidance for LW experiments.