“Polycrystal plasticity modeling, relationships with experiments, and applications”
Polycrystal modeling is a type of mesoscale model in which the properties of single crystalline grains (microscale) are related to the aggregate scale (macroscale) properties. After providing a brief overview of the history and concepts of polycrystal plasticity modeling, a number of examples will be provided which illustrate how the method can be used to extract greater value from diffraction-based characterization and mechanical testing. Examples will include the determination of controlling mechanisms of deformation in non-cubic metals and intermetallic compounds. Experimental methods will include TEM diffraction contrast imaging, electron backscatter diffraction (EBSD), neutron diffraction, and high-energy synchrotron X-ray diffraction. One application of the approach is the prediction of sheet metal forming limit curves used to design forming processes and tooling. Another application relates to materials microstructure design of compositionally complex alloys (CCAs) for optimal mechanical properties. Along the way, examples of how machine learning approaches can assist the processes of inverse problem solving and optimization will be highlighted. Time permitting, recent attempts to elucidate the single crystal properties of shape memory alloys will be discussed.
