Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods

TL;DR

This paper investigates how load paths influence the accuracy of parameter identification in plasticity models, employing Bayesian methods and stochastic simulations to improve model calibration under experimental uncertainties.

Contribution

It introduces a Bayesian parameter estimation approach using a Gauss-Markov-Kalman filter for plasticity models, accounting for load path effects and stochastic data.

Findings

Bayesian method improves parameter estimation accuracy.

Load path significantly affects parameter identification.

Stochastic simulation effectively mimics experimental data.

Abstract

To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model parameters are identified by applying an estimation using Bayes's theorem. The Gauss-Markov-Kalman filter using functional approximation is introduced and employed to estimate the model parameters in the Bayesian setting. Identified parameters are compared with the true parameters in the simulation, and the efficiency of the identification method is discussed. In the end, the effect of the load path on parameter identification is investigated.