|About this Abstract
||2021 TMS Annual Meeting & Exhibition
||Practical Tools for Integration and Analysis in Materials Engineering
||Batch Reification Fusion Optimization (BAREFOOT) Framework
||Richard Andrew Couperthwaite, Danial Khatamsaz, Abhilash Molkeri, Douglas Allaire, Ankit Srivastava, Raymundo Arroyave
|On-Site Speaker (Planned)
||Richard Andrew Couperthwaite
Developments in high-throughput materials manufacturing and testing have necessitated the development of design frameworks capable of making batch recommendations at each iteration in the optimization process. A Reification-Fusion based framework has already been developed and has been shown to greatly reduce the time required for the optimization of the mechanical properties of dual-phase steel. Since this framework is based on Bayesian optimization principles and so constructs Gaussian Process models of all the data used, it is an ideal candidate for a novel batch optimization procedure. This Batch optimization procedure samples from the Gaussian Process Hyper-parameter space to generate many realizations of the Gaussian Processes. This process removes any assumptions about the shape of the underlying function from the analysis and is capable of providing batch predictions. The current work has integrated these approaches into a combined framework that is capable of reducing the time and cost for optimizing material properties.