About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Novel Strategies for Rapid Acquisition and Processing of Large Datasets From Advanced Characterization Techniques
|
| Presentation Title |
Linking Energetic Material Sensitivity and Microstructure Variability Across Length Scales |
| Author(s) |
Daniel C. Bufford, William Bassett, David Damm, Robert Knepper, James Stewart, Jennifer Quinn, Dan Bolintineanu |
| On-Site Speaker (Planned) |
Daniel C. Bufford |
| Abstract Scope |
High-velocity impacts drive decomposition and energy release in energetic (explosive) materials. Local distributions of nm- to µm-scale pores strongly influence whether that energy release dampens or intensifies into a self-sustaining detonation wave over distances that may exceed hundreds of µm. Here we gain understanding regarding structure-property relationships between porosity and explosive initiation behavior through experimental microstructure characterization and shock physics simulations initialized from said microstructures. Sizes, morphologies, and distributions of microstructurally small pores were captured across large areas of pressed pellets by scanning electron microscopy (SEM), while machine learning enabled automated sorting and classification of SEM image features through n-point statistics. This work enables a better understanding of the role of local heterogeneities in the global response of the energetic materials and explains both performance differences stemming from as-manufactured variability and performance shifts following thermal exposures that alter initial microstructures in terms of features like density and pore size. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Powder Materials, Machine Learning |