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metamodel 2

ensemble of metamodels 1

hybrid error measure 1

laser beam welding 1

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parameter optimization 1

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Robust ensemble of metamodels based on the hybrid error measure

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 3, Pages 623-634 doi: 10.1007/s11465-021-0641-7

Abstract: However, challenge remains in the prior determination of the most suitable metamodel for a particular

Keywords： metamodel ensemble of metamodels hybrid error measure stochastic problem

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Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0703-5

Abstract: In fiber laser beam welding (LBW), the selection of optimal processing parameters is challenging and plays a key role in improving the bead geometry and welding quality. This study proposes a multi-objective optimization framework by combining an ensemble of metamodels (EMs) with the multi-objective artificial bee colony algorithm (MOABC) to identify the optimal welding parameters. An inverse proportional weighting method that considers the leave-one-out prediction error is presented to construct EM, which incorporates the competitive strengths of three metamodels. EM constructs the correlation between processing parameters (laser power, welding speed, and distance defocus) and bead geometries (bead width, depth of penetration, neck width, and neck depth) with average errors of 10.95%, 7.04%, 7.63%, and 8.62%, respectively. On the basis of EM, MOABC is employed to approximate the Pareto front, and verification experiments show that the relative errors are less than 14.67%. Furthermore, the main effect and the interaction effect of processing parameters on bead geometries are studied. Results demonstrate that the proposed EM-MOABC is effective in guiding actual fiber LBW applications.

Keywords： laser beam welding parameter optimization metamodel multi-objective