A multi-fidelity information fusion metamodeling assisted laser beam welding process parameter optimization approach
A multi-fidelity information fusion metamodeling assisted laser beam welding process parameter optimization approach
复制标题
多保真信息融合元建模辅助激光束焊接工艺参数优化方法
DOI：
10.1016/j.advengsoft.2017.04.001
复制
发表时间：
2017-08-01
影响因子：
4.8
通讯作者：
Wang, Chaochao
中科院分区：
文献类型：
--
作者：
Zhou, Qi;Yang, Yang;Wang, Chaochao
选择合理的激光焊接工艺参数对于获得良好的焊头轮廓，从而获得高质量的焊接接头是非常有帮助的。现有的LBW工艺参数优化方法要么是基于昂贵的物理实验，要么是基于低保真度（LF）的计算机模拟。本文提出了一种基于多保真度元模型的LBW工艺参数优化方法，该方法可以充分利用LBW计算机仿真和高保真度物理实验的不同保真度信息。该方法首先建立三维热有限元模型作为LF模型，并与LF元模型进行拟合。然后，以LF元模型为基础模型，利用HF物理实验对其进行缩放，构建了近似LBW工艺参数与焊头轮廓关系的MF元模型。采用两个指标来比较MF元模型与仅通过物理实验或计算机模拟构建的单保真度元模型的预测精度。结果表明，MF元模型在全局和局部精度上都优于单保真度元模型。最后，采用快速精英非支配排序遗传算法（NSGA-II）进行LBW工艺参数空间探索和多目标Pareto最优搜索。LBW验证实验验证了所获得的最优工艺参数的有效性和可靠性。(C) 2017 Elsevier Ltd.版权所有。
Selecting reasonable laser beam welding (LBW) process parameters is very helpful for obtaining a good welding bead profile and hence a high quality of the welding joint. Existing process parameter optimization approaches for LBW either based on cost-expensive physical experiments or low-fidelity (LF) computer simulations. This paper proposes a multi-fidelity (MF) metamodel based LBW process parameter optimization approach, in which different levels fidelity information, both from LF computer simulations and high-fidelity (HF) physical experiments can be integrated and fully exploited. In the proposed approach, a three-dimensional thermal finite element model is developed as the LF model, which is fitted with a LF metamodel firstly. Then, by taking the LF metamodel as a base model and scaling it using the HF physical experiments, a MF metamodel is constructed to approximate the relationships between the LBW process parameters and the bead profile. Two metrics are adopted to compare the prediction accuracy of the MF metamodel with the single-fidelity metamodels solely constructed with physical experiments or computer simulations. Results illustrate that the MF metamodel outperforms the single-fidelity metamodels both in global and local accuracy. Finally, the fast elitist non-dominated sorting genetic algorithm (NSGA-II) is used to facilitate LBW process parameter space exploration and multi-objective Pareto optima search. LBW verification experiments verify the effectiveness and reliability of the obtained optimal process parameters. (C) 2017 Elsevier Ltd. All rights reserved.