Reduced-Order Modeling of the Deformation Response of Polycrystalline Aggregates
Abstract
The primary objectives of this effort are to investigate: (a) the efficacy of applying machine learning algorithms in the determination of microstructure effects on anisotropic yield of key engineering components; and (b) reduced-order methodologies that allow for the linking of microstructural condition to macroscale deformation to aid in current life prediction model development. During this effort, researchers will work to develop a computational framework to allow for the simulation of component scale deformation processes with consideration from the microstructural condition. The majority of this effort will be focused on the implementation and development of a novel machine learning algorithms that will produce a reduced-parameter model describing the connection between crystal scale deformation phenomena and component scale plastic deformation phenomena
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 10, 2020
- Source ID
- FA86502015203
Entities
People
- Matthew Kasemer
Organizations
- Air Force Research Laboratory
- United States Air Force
- University of Alabama