From Limited Data to the Deformation Field in Metals: A Machine Learning Driven Approach

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Having warning before a catastrophic failure of a material occurs can save lives and reduce costs. Prior to failure a material may undergo internal changes which generate high-frequency stress waves, referred to as acoustic emissions. Measurements of acoustic emissions in metals provide a unique approach for quantifying defects and their movements. However, interpreting acoustic emissions is a longstanding challenge. This research will address this challenge by identifying and decoding the distinct acoustic emission signatures of each deformation mechanism with a combination of experiments and machine learning tools. This research will result in a unique method that endows experiments with a window into the fundamental deformation mechanisms that are not currently accessible from surface measurements alone. This will enable new basic knowledge of the behavior of metals during deformation. This research is also integrated with education and outreach. Results from this work will be integrated into a new course on machine learning for solid mechanics and materials engineering. Maryland high-school students from under-served/under-represented groups will also be engaged in research internship opportunities. We will utilize integrated physics-based modeling, machine learning, and experiments to: (1) develop a “digital twin’’ of acoustic emission experiments to forward predict the acoustic emission surface waves associated with complex slip avalanches during the deformation of single crystal Ni micropillars; (2) definitively assess/scrutinize existing phenomenological acoustic emission models in literature, and develop new physics-based theoretical models that identify the interconnections between dislocation-based plasticity and acoustic emission signals; (3) predict the true experimentally observed slip localization in the 3D volume from the surface acoustic emission measurements; (4) train deep operator networks (DeepONets) for forward predictions of acoustic emission and inverse predictions of the underlying deformation mechanisms; and (5) validate the forward and inverse predictions through coupled in situ scanning electron microscopy microcompression experiments and acoustic emission measurements on single-crystal Ni microcrystals. To close the loop between the developed models and the experiments, we will also utilize the trained DeepONets on the experimental results to gain fundamental understanding of the underlying deformation mechanisms during dislocation avalanches in micro-compression experiments, which are currently difficult to interpret based on surface measurements and load-displacement measurements alone.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.