About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
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Computational Techniques for Multi-Scale Modeling in Advanced Manufacturing
|
Presentation Title |
Machine-learning Informed Design of High-strength Gradient Metals for Additive Manufacturing |
Author(s) |
S. Mohadeseh Taheri-Mousavi, A. John Hart |
On-Site Speaker (Planned) |
S. Mohadeseh Taheri-Mousavi |
Abstract Scope |
The advent of additive manufacturing creates a need for computationally-efficient design approaches to determine complex relationships between local composition, microstructure, and resulting mechanical properties. In particular, if these attributes can be controlled with the dimensional and compositional fidelity needed to activate tailored strain gradient plasticity, i.e., the incompatibility of plastic strain near interfaces of the adjacent gradient components, one can achieve mechanical properties exceeding rule of mixture estimates. Here, we present a comprehensive strain gradient theory which accounts for the contribution of this incompatibility to strength and strain hardening. By calibrating the model with a bi-layered material system of copper and bronze, and by deep learning optimization, we reveal design motifs that achieve enhanced strength tailored to specific boundary conditions and load cases. Moreover, the framework is broadly applicable to meso-scale gradient systems and offers new perspectives to the discovery of next-generation structural alloys. |
Proceedings Inclusion? |
Planned: |