About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Plastic Deformation Reconstruction Based on Acoustic Emission Measurements |
Author(s) |
Junjie Yang, Yejun Gu, Daniel Magagnosc, Tamer Zaki, Jaafar El-Awady |
On-Site Speaker (Planned) |
Junjie Yang |
Abstract Scope |
Interpreting acoustic emission (AE) is a non-destructive evaluation (NDE) technique based on the elastic stress waves caused by irreversible deformations in materials. The core challenge is decoding AE signals and quantitatively determining the source, or deformation mechanisms. We adopt variational and machine-learning approaches to decode the complex interconnections between plastic deformation mechanisms in metals and the AE they emit. Surrogate measurements of AE associated with different dislocation mechanism were first obtained from full 3D elastodynamic-field solution incorporated into 3D Discrete Dislocation Dynamic simulations. Data assimilation procedures use the AE signals at individual sensors only, and reconstruct the plastic deformations. We show that the dislocation activities history in 3D can be accurately reconstructed using a single AE sensor. This approach enables the decoding of hard-to-interpret surface AE measurements and reconstruct plastic slip intrinsically in the material during deformation. |
Proceedings Inclusion? |
Undecided |