Gaussian-Process-Driven Adaptive Sampling for Reduced-Order Modeling of Texture Effects in Polycrystalline Alpha-Ti
Abstract
Data-driven tools for finding structure-property (S-P) relations, such as the Materials Knowledge System (MKS) framework, can accelerate materials design, once the costly and technical calibration process has been completed. A three-model method is proposed to reduce the expense of S-P relation model calibration: (1) direct simulations are performed as per (2) a Gaussian process-based data collection model, to calibrate (3) an MKS homogenization model in an application to α-Ti. The new methods are compared favorably with expert texture selection on the performance of the so-calibrated MKS models. Benefits for the development of new and improved materials are discussed.
- Publication:
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JOM - Journal of the Minerals, Metals and Materials Society
- Pub Date:
- August 2019
- DOI:
- 10.1007/s11837-019-03553-1
- Bibcode:
- 2019JOM....71h2646T