UNCERTAINTY QUANTIFICATION AND PROCESSING OPTIMIZATION FOR UHTC MANUFACTURING THROUGH AN ICME FRAMEWORK

Abstract

Standard ultra-high temperature ceramic (UHTC) manufacturing creates components with large differences in properties due to variability in microstructural critical flaw distributions. Critical flaws can be any irregularity in a component, such as a secondary phase, inclusion, crack, pore etc. This is problematic when designing reproducible UHTC components for Mach 6 hypersonic applications. The goal of this project is to build probabilistic characterization of processing-structure-properties (PSP) parameters and link them at each stage of UHTC processing in a way that allows for uncertainty propagation. This methodology has not been performed in the past due to the complex interrelations of UHTC PSP parameters that need to be deconvoluted. Thus, multi-fidelity PSP database development and effective integrated computational materials engineering (ICME) combined with statistical modeling is key to minimize uncertainty during UHTC manufacturing. This approach will consist of three key integrated thrusts- Thrust I- Manufacturing and rapid materials characterization- to populate a multi-fidelity PSP database using a combination of lower-fidelity in-lab experiments and modeling along with higher-fidelity synchrotron 3-D microstructural characterization experiments. Thrust II- Uncertainty quantification- to develop a PSP-ICME database in tandem with multi-fidelity statistical modeling using a Bayesian neural network approach. Thrust III- Validation of the multi-fidelity model- to verify that the statistical model can reduce uncertainty in UHTC manufacturing in terms of properties and microstructural flaws. The densification of ZrB2 will be used as a model system for this methodology as it is a prime candidate for ceramic matrix materials in hypersonic-ready ceramic matrix composites (CMCs).

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 07, 2023
Source ID
FA95502210064

Entities

People

  • Scott J McCormack

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force
  • University of California, Davis

Tags

Readers

  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development
  • Reinforced Composite Materials

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Hypersonics