期刊
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
卷 41, 期 2, 页码 309-324出版社
SPRINGER
DOI: 10.1007/s00158-009-0411-3
关键词
Parallel; Sampling; Metamodeling; Kriging interpolation; Drawbead; Sheet forming
In the present paper, a Kriging-based metamodeling technique is used to minimize the risk of failure in a sheet metal forming process. The Kriging-based models are fitted to data that are obtained for larger experimental areas than the areas used in low-order polynomial regression metamodels. Therefore, computational time and memory requirement can be an obstacle for Kriging for data sets with many observations. To improve the usability of the Kriging-based metamodeling techniques, a parallel intelligent sampling approach: boundary and best neighbor searching (BBNS) (Wang et al., J Mater Process Technol 197(1-3):77-88, 2008a) is suggested. Compared with the serial BBNS version, the sampling procedure is performed synchronously. Thus, larger sample size should be considered for real-life problems when multiple processors are available. Furthermore, the parallel strategy is prone to converge based on more samples. The performance of the parallel approached is verified by means of nonlinear test functions. Moreover, the drawbead design in sheet metal forming is successfully optimized by the parallel BBNS approach and Kriging metamodeling technique. The optimization results demonstrate that the parallel BBNS approach improves the applicability of the Kriging metamodeling technique substantially.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据