4.7 Article

Implementation and calibration of a mesoscale model for amorphous plasticity based on shear transformation dynamics

Journal

INTERNATIONAL JOURNAL OF PLASTICITY
Volume 145, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijplas.2021.103079

Keywords

Shear transformation zone (STZ); B; Glass material; B; Polymeric material; C; Elastic-plastic material; D; Finite elements

Funding

  1. Fond de la Recherche Scientifique de Belgique (F.R.S.-FNRS) [2.5020.11]
  2. Walloon Region
  3. FNRS [PDR -T.0178.19]

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A mesoscale numerical model based on STZ theory is proposed to predict the (visco)plastic deformation response of amorphous solids at the nano- and microscale, with reference calculations validating its potential. Emphasis is placed on the impact of time and space discretisation on the macroscopic response, and the dependence of predicted yield strength on fundamental model parameters is analyzed using a mean-field approximation. Guidelines for parameter identification through the mean-field approximation are provided, starting from experimental data.
A mesoscale numerical model based on shear transformation zone (STZ) theory is implemented in a commercial finite element software. The model is designed to predict the (visco)plastic deformation response of amorphous solids at the nano- and microscale. The theoretical framework relies on earlier models developed by Bulatov and Argon (1994a) and of Homer and Schuh (2009). We justify the potential of the computational model by conducting reference calculations for model metallic and polymeric glasses in plane strain compression. Emphasis is placed on the effect of time and space discretisation on the predicted macroscopic response. The dependence of the predicted yield strength upon the values of the fundamental model parameters is analysed via a mean-field approximation. The mean-field approximation is validated based on a series of simulations in model parameter space. We provide guidelines for a straightforward but consistent parameter identification method via the mean-field approximation while starting from experimental data.

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