MME Graduate Seminar: Dr. Aaron Tallman
This is a past event.
Friday, November 5 at 3:00pm to 4:30pm
College of Engineering and Computing, EC1107 10555 West Flagler Street Miami FL 33174
Modeling of the mechanical properties of engineering materials is governed by the expense of gathering high-quality materials testing data. Broadly, modeling of material properties and performance is critical to engineering design. Simpler empirical models of material properties have long addressed the demands of engineering design. In the empirical approach, models have a small, minimal set of parameters that are fit to the selected material. This calibration process must be repeated each time the material or processing is changed. Any trends observed in the empirical model parameters give very little help in optimizing microstructure. Over decades, newer material models have been developed to incorporate more scientific knowledge of the origins of plasticity and other deformation mechanisms. These physics-based models typically have many more parameters to calibrate than their empirical model counterparts. In fact, modern microstructure-sensitive models of plasticity have not displaced traditional empirical modeling approaches due to a number of practical concerns. For instance, microstructure-sensitive modeling requires testing of multiple microstructures in a much larger design of experiments than empirical models. Additionally, not all physics-based model parameters can be uniquely identified in the fitting process. But generally, these issues originate with scarcity of data.
Tremendous recent advances in data collection techniques have the potential to change the problem defining limitations of the material modeling workflow. In other words, data scarcity in materials modeling may soon give way to an abundance of data. 4D in-situ testing data and high-throughput, low-uncertainty testing data open many new avenues of inquiry. As we enter this new data-rich paradigm, questions become increasingly relevant: (1) How do we combine high-resolution data and low-cost data, (2) how do we incorporate both physics and testing data to improve materials models, and (3) how can data-driven modeling support engineering design in new ways? In this seminar, we will approach these questions by exploring some recent works on the multiscale modeling of plasticity in metals.
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