Publications

X-ray computed tomography is generally a primary step in characterization of defective electronic components, but is generally too slow to screen large lots of components. Super-resolution imaging approaches, in which higher-resolution data is inferred from lower-resolution images, have the potential to substantially reduce collection times for data volumes accessible via x-ray computed tomography. Here we seek to advance existing two-dimensional super-resolution approaches directly to three-dimensional computed tomography data. Multiple scan resolutions over a half order of magnitude of resolution were collected for four classes of commercial electronic components to serve as training data for a deep-learning, super-resolution network. A modular python framework for three-dimensional super-resolution of computed tomography data has been developed and trained over multiple classes of electronic components. Initial training and testing demonstrate the vast promise for these approaches, which have the potential for more than an order of magnitude reduction in collection time for electronic component screening.

Mechanical testing was conducted to collect the data needed to build a Xue-Wierzbicki (XW) fracture model for PH13-8 Mo H950 stainless steel (PH 13-8 SS). This model is intended for use in structural analysis of this material between room temperature and -40° C. Tests were performed on four different specimen geometries such that a range of stress states were characterized at room temperature and -40° C. Tensile tests on R5 tensile specimens were also performed to assess material anisotropy. Fracture toughness test were also conducted. The fracture toughness of this material at -40° C was 68% of the room-temperature value. Material strength generally increased with decreasing temperature while the opposite trend was observed for ductility. These trends were most pronounced for specimens with the largest stress triaxialities.