Abstract
An optimization methodology based on variable-fidelity (VF) metamodels and nondominated sorting genetic algorithm II (NSGA-II) for laser bead-on-plate welding of stainless steel 316L is presented. The relationships between input process parameters (laser power, welding speed and laser focal position) and output responses (weld width and weld depth) are constructed by VF metamodels. In VF metamodels, the information from two levels fidelity models are integrated, in which the low-fidelity model (LF) is finite element simulation model that is used to capture the general trend of the metamodels, and high-fidelity (HF) model which from physical experiments is used to ensure the accuracy of metamodels. The accuracy of the VF metamodel is verified by actual experiments. To slove the optimization problem, NSGA-II is used to search for multi-objective Pareto optimal solutions. The results of verification experiments show that the obtained optimal parameters are effective and reliable.
Highlights
Laser welding has been widely used in a variety of industrial applications due to its significant advantages including deep penetration, narrow heat-affected zone
To overcome the above mentioned shortcomings, this paper proposes an optimization methodology based on variable-fidelity (VF) metamodel [8, 9], which is able to combine the information from two different levels fidelity models, low-fidelity (LF) simulation model and high-fidelity (HF) physical experiment, for laser welding process parameter optimization
An optimization methodology using VF metamodel and nondominated sorting genetic algorithm II (NSGA-II) is proposed for process parameters optimization in laser bead-on-plate welding for stainless steel 316L
Summary
Laser welding has been widely used in a variety of industrial applications due to its significant advantages including deep penetration, narrow heat-affected zone. Metamodel assisted process parameters optimization methods can be divided into two distinct types: physical experiment-based optimization approach and simulation-based optimization approach. Physical experiment-based optimization approach obtains the data for constructing the metamodel by conducting laser welding experiments [1,2,3,4]. Compared with the physical experiment-based optimization approach, the most obvious advantage is that it can greatly shorten the design period and reduce the overall cost. This approach is difficult to ensure the accuracy of metamodel. This issue, to some extent, has limited their capability of guiding the actual laser welding processing
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