Collaborative Research: Learning Microstructure- and Temperature-Dependencies of Grain Boundary Plastic Deformation Localization via Multi-modal In situ Characterization

Project: Research project

Project Details


NON-TECHNICAL SUMMARY:Numerous failure mechanisms in engineering alloy parts are correlated to how deformation is transmitted across the boundaries between the microscopic crystals (grains) that comprise the part. Despite extensive study, definitive experimental evidence of the conditions at which deformation will and will not transmit across grain boundaries is elusive, particularly at extreme temperatures in conditions found during space travel or hypersonic propulsion. New electron microscopy and X-ray measurements, which can look in detail at and below the sample surface, are being used together to watch how deformation is transmitted across grain boundaries as it occurs. Using these measurements and machine-learning techniques, a set of rules are being established describing how deformation is transmitted as a function of temperature in a range of model atomic crystal structures representing various forms of commonly used structural alloys. These rules can then be used to improve the usage of existing alloys in the field and to design new high-performance alloy systems.TECHNICAL SUMMARY:State-of-the-art in situ characterization techniques are being taken advantage of to learn the temperature-dependence of microstructural conditions governing plastic deformation localization at and near grain boundaries (GBs) in cubic engineering alloys. A complimentary combination of in situ characterization techniques (high-resolution digital image correlation in the scanning electron microscope and synchrotron X-ray-based 3D reconstructions) capable of probing micromechanical response and microstructural state simultaneously are being used to interrogate the high-dimensional space of microstructural configurations that can exist across GBs in model face-centered cubic (FCC) and body-centered cubic (BCC) alloys. Existing machine-learning (ML) tools are also being used to perform automated classification of the large number of microstructural pairings probed during each in situ experiment and learn criteria for predicting the evolution of plastic deformation localization behaviors at GBs with temperature and associated deformation mechanism activation. In this effort, activation of twinning through cryogenic deformation and its effects on GB plastic deformation localization are being used to create a generalized framework with which the effect of activation of other deformation mechanisms, such as climb, cross-slip, and GB sliding, can be evaluated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Effective start/end date5/1/234/30/26


  • National Science Foundation: $298,843.00


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