NSF Award
2341922
This Faculty Early Career Development (CAREER) award supports research that will aim to answer the longstanding question: What causes materials to fail? Structural materials are the building blocks of modern lifestyle, supporting applications ranging from infrastructure to national security, yet the mechanisms underlying their failure are not well understood. The first stage of catastrophic failure is often the nucleation of small voids, through a complex and multifaceted process that has so far evaded simplified models. This project will leverage advanced machine learning methods, which can identify subtle trends in large datasets, with damage mechanics models to unravel the nuances of pore nucleation. The result will be a computer model that is able to rapidly screen materials to determine damage susceptibility. The educational part of this project is twofold. First, the project will make advances in machine learning-based mechanics of materials accessible to budding and amateur engineers and scientists through an internet-based application, called “Solid Genius.” Solid Genius will be freely available, allowing direct manipulation and exploration of the material model and its predictive capability through a user-friendly educational interface. Second, the project will develop a new graduate course to provide training for rising researchers in machine learning-based damage mechanics.<br/><br/>Experimental evidence indicates that grain boundaries are preferential sites for pore nucleation, but no meaningful correlations between grain boundary properties and failure likelihood have yet been conclusively established. A multi-channel convolutional neural network (MCCNN) machine learning framework is planned that will be able to identify potential pore nucleation sites in pristine microstructure. The framework will simultaneously account for both nonlocal properties (microstructure, grain texture, etc.) and local properties (pointwise curvature, inclination, etc.), synthesizing them against a training dataset to produce a reliable estimator of failure likelihood. Training data will consist of reconstructed experimental micrographs and EBSD data, divided into “failure” and “no-failure” partitions. The raw experimental data will then be enriched with secondary calculations and supplemental mechanics simulations to supply non-visible channels such as grain boundary energy and mechanical stress. The trained MCCNN framework will then be used in concert with a damage mechanics model to further probe the early-time behavior of pore initiation and growth. Development will include an emphasis on determining physical interpretability of each aspect of the MCCNN model, such as formalizing the connection between individual convolutional layers and feature segmentation in microstructure, to facilitate a more rigorous application of the framework to the problem of damage assessment.<br/><br/>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.
