A new POD-based approximate bayesian computation method to identify parameters for formed AHSS
International Journal of Solids and Structures, 2019, 160: 120-133.
Forming has significant influence on the material properties which are critical for the accuracy of simulations. However, at present, most of the existing works took the uniform raw mechanical properties of plate as the one after forming, which inevitably resulted in the inaccuracy simulation. In this study, in order to figure out the material properties of formed DP590, comprehensive experiments based on micro-indentation tests and digital image correlation (DIC) are presented. The versatile approximate Bayesian computation (ABC) inverse method is utilized to identify material parameters. Due to its difficulty of selection of summary statistics, a new flexible and feasible proper orthogonal decomposition (POD) based ABC framework is proposed. The POD can project the high dimensional observations into a much lower dimensional coefficient vector. Thus, we can make use of comparisons between simulated and observed coefficient vector of POD to determine the material parameters. With the POD, the ABC can circumvent the complex selection of summary statistics. In the computational process, the Neural Network (NN) is used to construct the nonlinear connection between material properties and its POD coefficient vector. Thus, the coefficient vector with given material properties can be determined efficiently with the well-constructed NN instead of time-consuming finite element (FE) simulations.
Material properties; Forming; Approximate bayesian computation; POD; Neural network