Abstract Scope |
In this talk, given a target microstructure, we present an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The problem is formulated as a multi-objective optimization problem. Each objective function measures the difference using the same microstructure descriptor between a candidate and a target microstructure. To significantly reduce the physical waiting wall-time, we enable the high-throughput feature by an asynchronous parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are demonstrated with kinetic Monte Carlo (kMC) simulation as a forward predictive model. For a given target microstructure, the target processing parameters are successfully recovered. Tran, A., Mitchell, J. A., Swiler, L., & Wildey, T. (2020). An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics (194) 80-92, Acta Materialia. |