Lightweighting of automobiles by use of novel low-cost, high strength-to-weight ratio structural materials can reduce the consumption of fossil fuels and in turn CO2 emission. Working towards this goal we achieved high strength in a low cost β-titanium alloy, Ti–1Al–8V–5Fe (Ti185), by hierarchical nanostructure consisting of homogenous distribution of micron-scale and nanoscale α-phase precipitates within the β-phase matrix. The sequence of phase transformation leading to this hierarchical nanostructure is explored using electron microscopy and atom probe tomography. Our results suggest that the high number density of nanoscale α-phase precipitates in the β-phase matrix is due to ω assisted nucleation of α resulting in high tensile strength, greater than any current commercial titanium alloy. Thus hierarchical nanostructured Ti185 serves as an excellent candidate for replacing costlier titanium alloys and other structural alloys for cost-effective lightweighting applications.

Understanding the electrochemical properties at a localized scale is critically important to comprehend the origin of corrosion and develop multifunctional materials with robust corrosion resistance, particularly at conjoined metal interfaces typically encountered in automobile manufacturing. Scanning electrochemical cell microscopy (SECCM) is an emerging technique which enables to study the corrosion of metal surfaces to be visualized at the microscopic level. In this work, we developed scanning electrochemical cell impedance microscopy (SECCIM) by combining SECCM with electrochemical impedance spectroscopy (EIS) and explored the unique advantages of using SECCIM to measure the corrosion kinetics on single-crystal Mg (0001) as the model surface using direct current and alternating current polarization techniques. Specifically, a theta capillary with a tip diameter of 10 μm filled with a 0.01 M NaCl electrolyte was used as a probe to perform spatially resolved potentiodynamic Tafel polarization and EIS. The combination of traditional SECCM with EIS led to the development of SECCIM and enabled us to study small interfacial events such as charge transfer, adsorption, and emergence of resistive oxide films on the surface using the distribution of relaxation time analysis. Furthermore, by comparing localized SECCIM measurements with bulk electrochemical measurements, we establish the reliability of SECCIM for the mapping of corrosion potential and associated charge-transfer resistance on the Mg (0001) surface. Our results indicate that SECCIM measurement with Tafel and EIS analysis will provide an unparalleled ability to characterize the pitting corrosion mechanism on the heterogeneous surface of mixed-metal alloys and metal joints.

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We investigate the methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium–molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on the F1 score. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions. We find that our newly developed microstructure representation describes image data well, and the traditional approach of utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relatively small, imbalanced original dataset of 272 images. To explore the applicability of generative methods for supplementing such limited datasets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image datasets are also discussed. Our work has implications for quantitative microstructure analysis and development of microstructure–processing relationships in limited datasets typical of metallurgical process design studies.

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Nuclear power research facilities require alternatives to existing highly enriched uranium alloy fuel. One option for a high density metal fuel is uranium alloyed with 10 wt% molybdenum (U-10Mo). Fuel fabrication process development requires specific mechanical property data that, to date has been unavailable. In this work, as-cast samples were compression tested at three strain rates over a temperature range of 400 to 800°C to provide data for hot rolling and extrusion modeling. The results indicate that with increasing test temperature the U-10Mo flow stress decreases and becomes more sensitive to strain rate. In addition, above the eutectoid transformation temperature, the drop in material flow stress is prominent and shows a strain-softening behavior, especially at lower strain rates. Room temperature x-ray diffraction and scanning electron microscopy combined with energy dispersive spectroscopy analysis of the as-cast and compression tested samples were conducted. The analysis revealed that the as-cast samples and the samples tested below the eutectoid transformation temperature were predominantly phase with varying concentration of molybdenum, whereas the ones tested above the eutectoid transformation temperature underwent significant homogenization. *Manuscript Click here to view linked References