Paper ID: 2410.08214 • Published Sep 25, 2024
Embedding an ANN-Based Crystal Plasticity Model into the Finite Element Framework using an ABAQUS User-Material Subroutine
Yuqing He, Yousef Heider, Bernd Markert
TL;DR
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This manuscript presents a practical method for incorporating trained Neural
Networks (NNs) into the Finite Element (FE) framework using a user material
(UMAT) subroutine. The work exemplifies crystal plasticity, a complex inelastic
non-linear path-dependent material response, with a wide range of applications
in ABAQUS UMAT. However, this approach can be extended to other material
behaviors and FE tools. The use of a UMAT subroutine serves two main purposes:
(1) it predicts and updates the stress or other mechanical properties of
interest directly from the strain history; (2) it computes the Jacobian matrix
either through backpropagation or numerical differentiation, which plays an
essential role in the solution convergence. By implementing NNs in a UMAT
subroutine, a trained machine learning model can be employed as a data-driven
constitutive law within the FEM framework, preserving multiscale information
that conventional constitutive laws often neglect or average. The versatility
of this method makes it a powerful tool for integrating machine learning into
mechanical simulation. While this approach is expected to provide higher
accuracy in reproducing realistic material behavior, the reliability of the
solution process and the convergence conditions must be paid special attention.
While the theory of the model is explained in [Heider et al. 2020], exemplary
source code is also made available for interested readers
[this https URL]