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1	Inkjet Printing of Graphene-Reinforced Zirconia Composite: Microstructures and Properties Pandit, Partha Pratim 26 July 2023 (has links) No description available. Mechanical Engineering
2	Toward privacy-preserving component certification for metal additive manufacturing Bappy, Mahathir Mohammad 13 August 2024 (has links) (PDF) Metal-based additive manufacturing (AM) has emerged as a cutting-edge technology for fabricating complex geometries with high precision. However, the major challenges to the wider adoption of metal AM technologies are process uncertainty-induced quality issues. Consequently, there is an urgent need for fast and reliable certification techniques for AM components, which can be achieved by leveraging Artificial Intelligence (AI)-enabled modeling. Developing a robust AI-enabled model presents a significant challenge because of the costly and time-intensive nature of acquiring diverse and high volume of datasets. In this context, the data-sharing attributes of Manufacturing-as-a-Service (MaaS) platforms can facilitate the development of AI-enabled certification techniques in a collaborative manner. However, sharing process data poses critical concerns about protecting users’ intellectual property and privacy since it contains confidential product design information. To address these challenges, the overarching goal of this research is to investigate how process data and process physics can be leveraged to develop in-situ component certification techniques focusing on data privacy for metal AM systems. This dissertation aims to address the need for novel quality monitoring methodologies by utilizing diverse data sources derived from a range of printed samples. Specifically, the research effort focuses on 1) the use of in-situ thermal history data and ex-situ X-ray computed tomography data for real-time layer-wise anomaly detection method development by analyzing the morphological dynamics of melt pool images; 2) the development of a framework to evaluate the design information disclosure of various thermal history-based feature extraction methods for anomaly detection; and 3) the privacy-preserving and utility-aware adaptive AM data deidentification method development that takes thermal history data as input.
3	FROM THEORY TO APPLICATION: THE ADDITIVE MANUFACTURING AND COMBUSTION PERFORMANCE OF HIGH ENERGY COMPOSITE GUN PROPELLANTS AND THEIR SOLVENTLESS ALTERNATIVES Aaron Afriat (10732359) 20 May 2024 (has links) <p dir="ltr">Additive manufacturing (AM) of gun propellants is an emerging and promising field which addresses the limitations of conventional manufacturing techniques. Overall, this thesis is a body of work which serves to bridge the gap between fundamental research and application of additively manufactured gun propellants.</p> Organic chemical synthesis Theoretical quantum chemistry Aerospace materials Powder and particle technology Composite and hybrid materials Polymers and plastics Solid mechanics additive manufacturing composite propellant gun propellant afriat method afriat isoconversional method solventless gun propellant vibration-assisted printing vibration assisted printing VAP 3DThermoDIC instrumented ram extruder low-vulnerability ammunition 3D internal geometries extrusion based additive manufacturing rheology of lova propellant lova propellant dynamic mechanical testing anisotropy gun propellant paste rheology clogging analysis high strain rate failure 3d digital image correlation thermal imaging powder gun gun firing ballistic performance

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