About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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Characterization of Minerals, Metals and Materials
|
Presentation Title |
Integrated Simulation, Machine Learning, and Experimental Approaches in Small-scale Mechanical Characterization of Materials |
Author(s) |
Xing Liu, Christos Athanasiou, Nitin Padture, Brian Sheldon, Huajian Gao |
On-Site Speaker (Planned) |
Xing Liu |
Abstract Scope |
The past decades have witnessed an increasing demand for characterizing mechanical properties of materials at small scales due to the miniaturization of devices. While tremendous advances in experimental techniques have been achieved, researchers are still struggling with the knowledge gap between the experimental measurements and the target mechanical property that needs to be extracted. To bridge this gap, we propose a new paradigm for small-scale mechanical characterization of materials, in which delicate experiments, high-fidelity simulations, and advanced machine learning (ML) techniques are seamlessly integrated. Its feasibility and value are demonstrated in small-scale fracture toughness measurements: microcantilevers bending experiments and pillar indentation splitting experiments. These applications highlight the potential transformative impact as well as the open challenges of data-driven approaches in small-scale materials characterization. |
Proceedings Inclusion? |
Planned: |
Keywords |
Characterization, Mechanical Properties, Modeling and Simulation |