Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel
Article
Tallman, AE, Kumar, MA, Castillo, A et al. (2020). Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel
. 9(4), 339-357. 10.1007/s40192-020-00181-5
Tallman, AE, Kumar, MA, Castillo, A et al. (2020). Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel
. 9(4), 339-357. 10.1007/s40192-020-00181-5
Predictions of the mechanical response of structural elements are conditioned by the accuracy of constitutive models used at the engineering length-scale. In this regard, a prospect of mechanistic crystal-plasticity-based constitutive models is that they could be used for extrapolation beyond regimes in which they are calibrated. However, their use for assessing the performance of a component is computationally onerous. To address this limitation, a new approach is proposed whereby a surrogate constitutive model (SM) of the inelastic response of 316H steel is derived from a mechanistic crystal plasticity-based polycrystal model tracking the evolution of dislocation densities on all slip systems. The latter is used to generate a database of the expected plastic response and dislocation content evolution associated with several instances of creep loading. From the database, a SM is developed. It relies on the use of orthogonal polynomial regression to describe the evolution of the dislocation content. The SM is then validated against predictions of the dead load creep response given by the polycrystal model across a range of temperatures and stresses. When the SM is used to predict the response of 316H during complex non monotonic loading, extrapolating to new loading conditions, it is found that predictions compare particularly well against those from the physics-based polycrystal model.
