SUBJECT: Ph.D. Proposal Presentation
BY: Wei Huang
TIME: Wednesday, April 17, 2024, 11:00 a.m.
PLACE: Kendeda Bldg, Room 118,
TITLE: PhD Proposal Presentation
COMMITTEE: Dr Steven Y. Liang, Chair (ME)
Dr Hamid Garmestani (MSE)
Dr Thomas Kurfess (ME)
Dr. Shreyes N. Melkote (ME)
Dr. Jianjun Shi (ISYE)


Despite the increasingly powerful bio-inspired artificial intelligence (AI) and data- driven industrial revolution, the analytical philosophies of science established about 400 years ago still undoubtedly hog academic research. Their combination has shown outstand- ing performance in industries and academia. In the long history of human eras, various materials and manufacturing processes play a core role. Emerging additive manufacturing (AM) provides a green and sustainable manufacturing approach in an inverse philosophy compared to traditional procedures, benefiting the current global decarbonization strategy. However, AM still needs to address many challenges due to its multi-physical processes in various materials systems or multiple applying situations before implementation in more fields and replacing more places of traditional manufacturing. Specifically, the primary aim of this investigation is to study the microstructural changes that affect material properties, such as elastic modulus and Poisson’s ratio. These changes affect the material’s perfor- mance, including residual stress, fractures, etc. To achieve this, the characterization of the microstructure of materials, mainly the surface/textures, grain size, and defects, if neces- sary, is of great importance. The texture and grain size simulation for multi-phase materials systems based on accurate physical stimuli modeling of processing will also be conducted. The influence of microstructural evolution on material properties will be measured. Several paradigms will be constructed to optimize manufacturing processes, utilizing combined ad- vantageous analytical and data/machine learning or semi-analytical frameworks. Experi- mental results will be used to validate the robustness of the models. This study bridges the gap between micro and macrostructures and the properties of materials in AM, potentially revolutionizing the industry and inspiring a new philosophy for science.