Identifying optimal process parameters in deep penetration laser welding by adopting Hierarchical-Kriging model

Abstract

The quality of welding joints is largely dependent on the laser welding process parameters. In this work, an integrated optimization methodology by combining the Hierarchical-Kriging model and non-dominated sorting genetic algorithm II (NSGA-II) is developed for identifying the optimal process parameters in deep penetration laser welding. Firstly, a three-dimensional thermo-mechanical finite element model is developed as a low-fidelity (LF) model, while the laser welding experiment is taken as a high-fidelity (HF) model. Then, the data sets from these two different levels fidelity models are integrated by Hierarchical-Kriging model to build the relationships between welding process parameters and bead profile and angular distortion. Secondly, the NSGA-II algorithm is employed to obtain the multi-objective Pareto optimal solutions based on the constructed Hierarchical-Kriging model. Finally, the effectiveness and reliability of the obtained optimum are verified by laser welding experiments. Results illustrate that the developed integrated optimal method provides a promising way to identify favorable process parameters for generating a desirable bead profile and reducing the angular distortion in deep penetration laser welding.