| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
|
| Presentation Title |
Machine Learning Based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-matrix-nano-composites |
| Author(s) |
Md Shahrier Hasan, Wenwu Xu |
| On-Site Speaker (Planned) |
Md Shahrier Hasan |
| Abstract Scope |
The mechanical properties of Metal-Matrix-Nano-Composites (MMNC) have demonstrated significant enhancement with the presence of a small fraction of nano-inclusions. To understand the effect of nano-inclusions on the macro-scale or continuum level properties of MMNCs, a multi-scale modeling is necessary, one that can pass the atomistic mechanism-dependent information to the continuum calculations. In this work, a novel Machine Learning (ML) enabled hierarchical multi-scale modeling was developed by coupling the atomistic Molecular Dynamics (MD) simulations with the macro-scale Finite Element Method (FEM) to understand and make predictions on how the nano-inclusions affect the macroscopic properties of MMNCs. At first, MD simulations at various loading conditions and nano-inclusion structures were conducted to generate constitutive data which are subsequently used to train various ML classification and regression models. These ML models are implemented as a constitutive material model in the macro-scale FEM, achieving the multi-scale modeling of mechanical deformation of MMNCs. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Machine Learning, Modeling and Simulation, Mechanical Properties |