Multi-Objective Parameter Optimization of Fiber Laser Welding Considering Energy Consumption and Bead Geometry

Exploiting energy-saving potentials while obtaining ideal processing results is highly sought for in fiber laser welding (FLW) applications. However, little is known about the correlation between energy consumption and bead geometry of FLW, which is largely determined by processing parameters. In this study, to find the optimal processing parameters of FLW, an ensemble of variable neighborhood search–based gene expression programming (VNS-GEP) and black-box metamodels (EGBM) is presented, combined with non-dominated sorting genetic algorithm (NSGA-II) to form the proposed optimization methodology. Firstly, the optimal weight coefficients of VNS-GEP and two black-box metamodels (Kriging and SVR) are determined considering the leave-one-out generalized mean square error. Then, the EGBM is used to establish the relationship between processing parameters (laser power, welding speed and defocus distance) and response results (energy consumption and bead geometry). Additional experiments are then performed to validate the accuracy of EGBM. Analysis of variance (ANOVA) is carried out to study the main effects of the processing parameters on response results. After that, NSGA-II is employed based on EGBM to approximate the Pareto front of processing parameters with minimal total energy consumption (TEC) and maximal depth-to-width ratio (DWR). Finally, experimental validations show that the obtained solutions can achieve ideal DWR and significant reductions in TEC. In conclusion, the proposed hybrid methodology, EGBM-NSGA-II, can facilitate obtaining optimal welding processing parameters and provide a reliable empirical basis for low-energy laser manufacturing. Note to Practitioners—This study considers a practical problem of energy-aware processing parameter optimization encountered in fiber laser welding applications. Besides, the optimization methodology can be generalized to other laser processes, optimization objectives and processing conditions. This study presents an ensemble of improved gene expression programming and black-box metamodels (EGBM). An EGBM-based optimization methodology is proposed to find the optimal laser welding parameters. The ensemble combines the strengths of different metamodels, reduces the risk of using an incorrect metamodel. Note that the engineers profit from the reduced trials and time to obtain the optimal parameters for ideal results and energy-saving.