Meeting ID: 955 8323 3960  Passcode: 064702 
Event Type:
MSE Grad Presentation
Talk Title:
Self-consistent Modeling and Material Property Analysis of an Additively Manufactured Polycrystalline Material
Zoom Videoconferencing  

Committee Members:     

Prof. Hamid Garmestani, Advisor, MSE  

Prof. Steven Liang, ME  

Prof. David L. McDowell, ME  

Prof. Preet Singh, MSE  

Prof. Saïd Ahzi, MSE  


Self-consistent Modeling and Material Property Analysis of an Additively Manufactured Polycrystalline Material


Material modeling is the central theme of this thesis.  Experimentation of titanium alloys and composites provided the background knowledge of the time and financial costs associated with testing and re-testing properties in a forensic, trial-and-error manner.  The model discussed in this thesis is a self-consistent model that merges material properties and process parameters to generate a unique microstructure for the titanium 6Al-4V (Ti-6-4) alloy produced using selective laser melting additive manufacturing SLM-AM.  The material texture is generated by first calculating melt pool geometries using Rosenthal Solution equations and Bunge matrix transformations.  The result is a single-phase representation of a liquidus, BCC beta titanium phase deposited over a random-orientation substrate.  The texture is then transformed into a two-phase alpha (HCP)-beta microstructure through transformation pathways modeled based on mechanisms discovered in other studies.  The final texture product can then be input into other models capable of computing mechanical properties based on texture inputs.  Though no model can be fully comprehensive in simulating material nature and behavior, the self-consistent model in this thesis adapts enough experimental data and follows enough phenomenological observations within the field of material science and engineering to produce simulated samples capable of achieving realistic property values. The model is scripted in a manner where it can be adapted for alternative materials by inputting different properties and tailoring the process settings.  Multiple benefits arise from being able to model material microstructures without the need to physically test real-world samples.  There is a substantial time savings in being able to quickly adjust properties and formulations.  Expensive equipment, materials, and labor can all be avoided, and a larger testing matrix can be executed through this approach.