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An Initial Study Of Properties Of Monolithic Silicon Dioxide Parts Produced By The Ceramic On-Demand Extrusion Process, Sam Sujit Choppala
An Initial Study Of Properties Of Monolithic Silicon Dioxide Parts Produced By The Ceramic On-Demand Extrusion Process, Sam Sujit Choppala
Master's Theses
The ceramic on-demand extrusion (CODE) process is an additive manufacturing process for technical ceramics, which features an oil bath and partial drying to print stronger green bodies due to better preservation of the printed specimen structure and reduction of uneven part evaporation. A 3D printer capable of performing the CODE process is built with subsystems including motion, extrusion, and heating, and is controlled through LinuxCNC. The CODE printer and process is tested with silicon dioxide as the technical ceramic. High strength silicon dioxide parts are essential in various present-day industries such as dental, medical, and semiconductor. A 55vol% solids loading …
Acoustic Cloak Design Using Generative Modeling And Reinforcement Learning, Linwei Zhuo
Acoustic Cloak Design Using Generative Modeling And Reinforcement Learning, Linwei Zhuo
Master's Theses
Metamaterials are engineered composites that can exhibit acoustic, electromagnetic, elasto-dynamic and mechanical properties that are not found in natural materials Due to the complexity of the target objective functions, it is difficult to find the globally optimized solutions in the inverse design of metameterials. This thesis proposes and outlines two model, a gradient-based optimization method combined with generative networks (2D-GLOnets) and a reinforcement learning (RL) model, that can find the optimized metamaterial structures across a wide range of parameters. By perturbing the positions of each cylindrical scatterer in a planar configuration, 2D-GLOnets and the RL model with Deep Deterministic Policy …
Acoustic Lens Design Using Machine Learning, Wei-Ching Wang
Acoustic Lens Design Using Machine Learning, Wei-Ching Wang
Master's Theses
This thesis aims to contribute to the development of a novel approach and efficient method for the inverse design of acoustic metamaterial lenses using machine learning, specifically, deep learning, generative modeling, and reinforcement learning. Acoustic lenses can focus incident plane waves at the focal point, enabling them to detect structures non-intrusively. These lenses can be utilized in biomedical engineering, medical devices, structural engineering, ultrasound imaging, health monitoring, etc. Finding the global optimum through a traditional iterative optimization process for designing the acoustic lens is challenging. It may become infeasible due to high dimensional parameter space and the compute resources needed. …