About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Prediction of Anisotropic Plastic Flow from Indentation Responses Via Neural Networks Combined with Finite Element Analysis |
Author(s) |
Kyeongjae Jeong, Kyungyul Lee, Siwhan Lee, Jinwook Jung, Hyukjae Lee, Nojun Kwak, Dongil Kwon, Heung Nam Han |
On-Site Speaker (Planned) |
Kyeongjae Jeong |
Abstract Scope |
In this study, anisotropic plastic behavior is predicted from spherical indentation responses using neural networks (NNs) trained with information collected by finite element (FE) simulations. We present a robust FE–NN modeling approach that inversely solves the problem of leading the mechanical properties of anisotropic materials from the load-depth curve, pile-up/sink-in, and residual in-plane displacement field. The predictive performance of the FE–NN model is evaluated by comparison with uniaxial tensile curves measured in the rolling, diagonal, and vertical directions. Furthermore, we propose that the Knoop indenter enables accurate inverse analysis without additional indentation data other than the load-depth curve. By paying attention to the characteristics of the Knoop indenter whose load-depth curve varies depending on the rotation angle about the indentation axis, as opposed to the spherical indenter, we discuss the potential capability of the Knoop indenter in the inverse analysis. |
Proceedings Inclusion? |
Undecided |