MAP123-EPF: A mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain. Direct numerical simulation based on experimental stress–strain data without explicit constitutive models is an active research topic. In this paper, a mechanistic-based, data-driven computational framework is proposed for elastoplastic materials undergoing finite strain. Harnessing the physical insights from the existing model-based plasticity theory, multiplicative decomposition of deformation gradient and the coaxial relationship between the logarithmic trial elastic strain and the true stress is employed to perform stress-update, driven by two sets of the specifically measured one dimensional (1D) stress–strain data. The proposed approach, called MAP123-EPF, is used to solve several Boundary-Value Problems (BVPs) involving elastoplastic materials undergoing finite strains. Numerical results indicate that the proposed approach can predict the response of isotropic elastoplastic materials (characterized by the classical J2 plasticity model and the associative Drucker–Prager model) with good accuracy using numerically/experimentally generated data. The proposed approach circumvents the need for the several basic ingredients of a traditional finite strain computational plasticity model, such as an explicit yielding function, a hardening law and an appropriate objective stress rate. Demonstrative examples are shown and strengths and limitations of the proposed approach are discussed.
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- Krokos, Vasilis; Xuan, Viet Bui; Bordas, Stéphane P. A.; Young, Philippe; Kerfriden, Pierre: A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features (2022)
- Tang, Shan; Yang, Hang; Qiu, Hai; Fleming, Mark; Liu, Wing Kam; Guo, Xu: MAP123-EPF: a mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain (2021)