Opportunity at Air Force Research Laboratory (AFRL)
Data Fusion and Analysis for Multi-Scale Mixed Modality Meso-to-Macroscale Microstructural Feature Characterization
Materials & Manufacturing, RX/Materials State Awareness & Supportability
||Wright-Patterson AFB, OH 454337817
|Wertz, John Nicholas
The capability of an aerospace material depends on multiscale material properties. One example is found in certain titanium alloys, which exhibit spatial clustering of similarly oriented grains known as microtextured regions. Microtextured regions affect material performance as a function of their size, shape, and common spatial distribution. The state-of-the-art method for characterizing surface meso-to-macroscale microstructural features like microtexture requires laboratory-scale scanning electron microscopy and careful material preparation. Sub-surface characterization requires these methods be applied to a material cross-section, destroying the component during the characterization process. A fast, robust, nondestructive method of determining the size, shape, and spatial distribution of features like microtexture would revolutionize broad areas of materials science, including process modeling, materials tailoring, and risk assessment. This leap in capability can be achieved through application of cutting edge data fusion, statistical analysis, and signal processing applied to multi-spectral nondestructive sensing.
Areas of interest for this work include:
- Data fusion algorithms to intelligently combine information from various multi-spectral nondestructive sensors
- Statistical methods for evaluating fused data to characterize meso-to-macroscale microstructural features
- Smart sensing techniques for rapid quantification of component-level features
- Novel sensor designs to enable/improve surface and sub-surface characterization of meso-to-macroscale microstructural features
- Clum, C., Mixon, D., & Scarnati, T. (2019). Matching Component Analysis for Transfer Learning. arXiv preprint arXiv:1909.01797.
- Iglesias, M., Lu, Y., and Stuart, A. (2016) “A Bayesian Level Set Method for Geometric Inverse Problems,”Interfaces and Free Boundaries, 18(2): 181-217.
- Dunlop, M., Iglesias, M., and Stuart, A. (2017) “Hierarchical Bayesian Level Set Inversion,” Statistics and Computing, 27(6): 1555-1584.
- Homa, L., Cherry, M., and Sparkman, D. (2019) “A Bayesian Level Set Method for Microtexture Region Characterization using Eddy Current Data,” Review of Progress in Quantitative Nondestructive Evaluation. Abstract available online: https://www.iastatedigitalpress.com/qnde/article/id/8537
data fusion; nondestructive evaluation; data analysis; microstructure; materials characterization
Open to U.S. citizens
Open to Postdoctoral and Senior applicants
$3,000 Supplement for Doctorates in Engineering & Computer Science
Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the number of years of experience past their PhD.