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A Robust Inverse Method Based on Least Square Support Vector Regression for Johnson-cook Material Parameters

Hu Wang1, Weiyi Li1, Guangyao Li1
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China

Computers, Materials & Continua 2012, 28(2), 121-146. https://doi.org/10.3970/cmc.2012.028.121

Abstract

The purpose of this study is to propose a robust inverse method for estimating Johnson-Cook material parameters. The method is shown through illustrative examples for two different advanced high strength steel (AHSS) materials (DP980 and TRIP780) using set of data from impact experiments with different velocities. Compared with widely mixed numerical experimental methods, the suggested inverse method has the capability to guarantee the robustness of the obtained parameters by considering uncertainties. The inverse problem is converted into multi-objective optimization problems. Furthermore, in order to improve the performance in efficiency and accuracy, metamodeling techniques and global optimization method are integrated. The final results demonstrate that the experimental and simulation curves are well matched based on identified by the suggested robust inverse method.

Keywords

Robust inverse method, John-Cook model, metamodel, least square support vector regression

Cite This Article

H. . Wang, W. . Li and G. . Li, "A robust inverse method based on least square support vector regression for johnson-cook material parameters," Computers, Materials & Continua, vol. 28, no.2, pp. 121–146, 2012.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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