Optimization of crystal plasticity parameters with proxy materials data for alloy single crystals

Multiscale modelling approaches have demonstrated ample value in understanding, predicting, and engineering materials response. While increasing computational power has aided in modelling atomic behaviour from first principles, mesoscale mechanisms such as intergranular failure or crack initiation still rely strongly on correlative models. Crystal Plasticity models have been extensively used to relate process-property-structure in metallic materials including mesoscale effects such as texture, microplasticity, and failure variability. However, models still suffer from low predictive power at the grain scale, which leads to poor damage prognosis outside the experimental calibration set. In addition to model form error, mesoscale uncertainty is dominated by an inadequate model parameterization that arises from calibration exclusively to macroscopic experimental data. This work explores parameter uncertainty in Crystal Plasticity models and proposes a hybrid physic-based and numerical optimization approach to identify parameters associated to mesoscale strengthening in FCC metals and alloys. The strength and novelty of the approach rely on calibrating parameters independently using single-crystal and polycrystal stress–strain curves. We further demonstrate that multiple materials can be incorporated into a single optimization algorithm to robustly quantify mesoscale material-invariant parameters. These values are then used to blindly predict the response of single- and poly-crystals engineering alloys. As a result, our approach mitigates modelling uncertainty by augmenting the data for calibration with single crystal experiments from different materials with similar dislocation structures (i.e., proxy materials). The results provide the basis for a robust parameterization of crystal plasticity models that can predict single- and poly-crystal responses for engineering alloys even in the absence of direct experimental data.