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Dissertation Proposal Defense – Eric Hoar
MSE Grad Presentation
Friday, May 18, 2018 - 1:00pm
MRDC 3515, Hightower Conference Room
Committee Members: Prof. Hamid Garmestani, Advisor, MSE Prof. Steven Liang, ME Prof. Chaitanya Deo, NRE Prof. Surya Kalidindi, MSE Dr. Peter Bocchini, Boeing R&T
"Materials-Affected Manufacturing: Inverse Model for the Simulation of Microstructure Evolution in Ti64 through Processing"
The creation of an inverse model for the simulation of texture evolution due to mechanical processing could lead to the identification of optimized processing procedures for various industrial components. Alloys such as AA7075 and Ti64 have increased importance in the aerospace and aeronautics industries while the Zr-18%Nb alloy discussed in this proposal is a surrogate for a U-Nb alloy which is important in the field of nuclear forensics. Currently in industry, new processing procedures are created through trial-and-error experiments to identify procedures which result in the desired properties and component shape. It is known that there exists a link between properties and microstructure for all crystalline materials. Therefore, if the properties of the material are known then the microstructure of the material can be calculated. However, the proposed work will not focus on the link between properties and microstructure. Instead, this work will focus on the link between the microstructure before processing and the microstructure after processing. The inverse model proposed here would take the microstructure after processing as an input, gather information on texture, grain size, and grain morphology, then output a microstructure which when processed using a specific processing procedure will result in the desired microstructure after processing. This model thus, would result in an elimination of the trial-and-error experiments currently performed in industry and would allow for the optimization of material microstructure and indirectly material properties. The research proposed here will focus on the microstructure evolution of AA7075, Ti64 and Zr-18%Nb through the mechanical processes of rolling and milling. The results shown here will illustrate our current modeling success using the orientation distribution function (a one-point correlation function) as the microstructural information for turning in both AA7075 and Ti64 as well as hot rolling and annealing in Zr-18%Nb. The proposed research will extend the current inverse model to allow for the use of two-point correlation functions which will greatly increase the amount of microstructural information that is known. Ultimately, the goal of the research proposed here is to create an analytical inverse model which uses two-point correlation functions to identify the microstructural evolution in AA7075, Ti64, and Zr-18%Nb due to rolling, milling, and machining processes.