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Data-driven approaches to defining material property and performance relationships of armor ceramics undergoing dynamic loading
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- Author / Creator
- Yang, Shuo
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This thesis utilizes a combined numerical and machine learning approach to explorethe performance of an alumina ceramic tile undergoing high-velocity impact. The fi-nite element model is established by incorporating a user-defined Johnson-Holmquist-Beissel (JHB) material model within the framework of smoothed particle hydrody-namics (SPH) in LS-DYNA finite element software. The computational frameworkis validated across a range of conditions by matching the simulation results fromboth plate impact experiments and ballistic testing from the literature. Once vali-dated, the model is used to generate training data sets for an artificial neural network(ANN) to predict the residual velocity and projectile erosion of an alumina ceramictile undergoing high-velocity impact in the SPH framework. The ANN is then used toperform a sensitivity analysis involving exploring the effect of mechanical properties(e.g., strength and shear modulus) and impact simulation geometries (e.g., the thick-ness of ceramic tile) on material performance (i.e., residual projectile velocity anderosion). Overall, this study shows the capability of the hybrid FEM-ANN approachin studying the high-velocity impact on ceramic tiles and is applicable to guide thestructural-scale design of ceramic-based protection systems.
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- Subjects / Keywords
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- Graduation date
- Spring 2023
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.