Numerical Estimation of Material Properties in the Electrohydraulic Forming Process Based on a Kriging Surrogate Model. (10th June 2020)

• Record Type: Journal Article
• Authors: Woo, Mina; Lee, Kyunghoon; Song, Woojin; Kang, Beomsoo; Kim, Jeong
• Other Names: Lancioni Giovanni Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-06-10
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/3219829
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : High-speed forming processes, such as electrohydraulic forming, have recently attracted attention with the development of forming technology. However, because the high-speed operation (above 100 m/s) raises safety concerns, most experiments are conducted in a closed die, which hides the forming process. Therefore, the experimental process can only be observed in a numerical simulation with accurate material properties. The conventional quasistatic material properties are improper for high-speed forming simulations with high strain rates (>10 2 s −1 ). In this study, the material properties of Al 6061-T6, which reflect the deformation behavior in the high-strain-rate region, were investigated in a numerical approach based on a reduced order model and a surrogate model in which the numerical results resemble the experimental results. The strain rate effect on the material was determined by the Cowper–Symonds constitutive equation, and two strain rate parameters were predicted. The surrogate model takes two material parameters as inputs and outputs a weighting coefficient calculated by the reduced order model. The surrogate model is based on the Kriging method to reduce the simulation cost. Next, the optimal material parameters that minimize the error between the surrogate model and the experiments are estimated by nonlinear least-squares optimization using a genetic algorithm and the constructed surrogate model. The predicted optimal parameters were verified by comparing the results of the experiment, numerical simulation, and surrogate model.