About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Simulation of Mechanical Properties of TPMS-based Osteoporotic Bone by the Neural Network-Enhanced Finite Element Method |
Author(s) |
Yan-Zhen Chen, Chu-Hao Wang, Tsung-Yeh Hsieh, Tsung-Hui Huang, Cheng-Che Tung, Po-Yu Chen |
On-Site Speaker (Planned) |
Yan-Zhen Chen |
Abstract Scope |
Osteoporosis is one of the common symptoms for the elderly people around the world. Owing to the complex structures of cortical and trabecular bones, billions of elements have to be built inside the finite element analysis (FEA) model, which makes the simulation time-consuming in both modeling and solving processes. In this work, we aim to prune the miscellaneous simulation cost by integrating triply periodic minimal surfaces (TPMS) and neural network (NN) approaches. We generate a parameterized density neural network enhanced finite element method (NN-FEM), which involved representative volumetric element (RVE) construction, off-line machine learning, and deployment on a homogenized surrogate model. By assigning the TPMS parameters to this novel scheme, modeling and calculation costs can be reduced more than 100 times. The mechanical properties of normal and osteoporotic femur bones are simulated and compared. The scheme provides a new and efficient way to further mechanical properties of diseased mineralized tissues. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Mechanical Properties, Biomaterials |