| Abstract Scope |
In this presentation, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through the CPFEM model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which improves the manufacturing feasibility. To solve this problem, we utilize a crystal plasticity finite element model (CPFEM) and we employ a recently developed uncertainty quantification (UQ) framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. We combine this approach with a machine learning (ML) model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in materials design. |