PhD Proposal by Mostafa Mahdavi

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Under the provisions of the regulations for the degree


on Monday, March 30, 2020

10:30 AM
REMOTE: https://bluejeans.com/4266435773


will be held the





Mostafa Mahdavi


"Materials-Affected Manufacturing: Simulating the Inelastic Properties of Polycrystalline Microstructures"


Committee Members:


Prof. Hamid Garmestani, Advisor, MSE

Prof. Steven Liang, ME

Prof. Naresh Thadhani, MSE

Prof. Preet Singh, MSE

Prof. Chaitanya Deo, MSE/NRE




Simulating the mechanical properties of a microstructure has been a widely studied topic in the field of manufacturing, mechanical, and materials engineering in recent decades. Different studies proved that microstructure (includes information such as phase, grain, and crystallographic texture) plays an important role in the mechanical properties. There are different approaches to predict these properties by using a part of the microstructure information. However, using crystallographic texture information as well as phase information forces the existed models to deal with having a high computational cost. This study introduces a new model that computationally is much faster than the existed models (e.g. finite element methods) and the results have a reasonable error compared to experiments. This model would be capable to consider the texture as well as phase information for polycrystalline microstructures with more than 4000 grains. This model aims to use statistical continuum mechanics theory in order to predict the inelastic properties of polycrystalline materials considering the phase and crystallographic texture distribution in the microstructure. For the first step, we modified a former model, which was developed by Garmestani et al. for isotropic materials, to simulate the inelastic behavior of anisotropic materials as in reality most materials are anisotropic. 


At this point, the model only uses phase information in the microstructure and outputs 2D inelastic properties response while the former model is limited to a two-phase isotropic material. For the next step, we are planning to take crystallographic texture information into account as well. In that case, we are going to use single crystal behavior of each phase for N-phase materials to predict the inelastic properties in any direction. The beauty of this model is that the input (microstructure) for these models can be either 3D (e.g. CT scan data to represent the phase, grain, and texture in 3D) or 2D (e.g. optical/electron microscopy images for phase information and X-ray diffraction data for texture information representation in 2D) and the output can also be either 3D or 2D depending on the input.    

The studied alloys in this study are Ti-6Al-4V and Inconel, these alloys are widely used in the aerospace and aeronautics industries. At this point, we use different materials characterization methods to extract the microstructure information for conventionally produced and additively manufactured (AM) materials. However, for the next step, we are going to develop a model to predict the microstructure of AM materials using the additive manufacturing process parameters. Then, use that predicted microstructure to predict the corresponding mechanical properties to those process parameters. Therefore, the final goal is to deliver a model for any polycrystalline material where the inputs are the additive manufacturing process parameters and the outputs are the microstructure and inelastic properties.


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