Creating Tailorable Material Behaviors through Intelligent Material Design and Multi-scale Additive Manufacturing
Abstract
The advent of new additive manufacturing techniques has enabled the ability to create arbitrary, complex three-dimensional shapes with topological feature sizes spanning orders of magnitude in length-scales. Current design and material creation approaches are not able to create and tailor the entire mechanical behaviors of materials, including their stress strain evolutions, deformation, energy absorption landscape, hysteresis, failure modes and recovery, which cannot be described by a set of simplistic values.This project develops a set of novel architected material creation methodologies to enable fully tailorable material behaviors through machine learning and multi-scale additive manufacturing -- where a designer inputs desired stress-strain curves that describes the desired mechanical behavior which will then be inversely designed, and created into a material that fully replicates the input behavior. We will achieve this by developing artificial intelligence to learn and design hierarchical 3D micro-architectures that displays target stress-strain response. Property and structure signatures derived from arbitrary 3D micro-architectures at disparate length-scales will drive the machine learning process for creating materials that replicate arbitrary engineering stress and strain responses. These inversed design output, will then be created through a suite of additive manufacturing techniques with materials, features and resolutions which are otherwise not possible through traditional manufacturing approaches. These new design and manufacturing routes represent a new paradigm for material by design, where the entire material mechanical behaviors throughout the service period containing a rich amount of material property information can be precisely created. A selection of naval relevant metallic alloy and composite feedstock will be experimentally created and verified.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 27, 2018
- Source ID
- N000141812553
Entities
People
- Xiaoyu Zheng
Organizations
- Office of Naval Research
- United States Navy
- Virginia Tech