Probing corrosion using a simple and versatile in situ multimodal corrosion measurement system.

In this work, we have developed a unique in situ multimodal corrosion system that is capable of acquiring electrochemical data, sample imaging/visualization and hydrogen collection, simultaneously. Each of these modalities yield valuable information pertaining to the ongoing corrosion process. Combining them can yield holistic information on the role of microstructure, processing history, presence of coatings, etc., on the sequence of steps occurring during the corrosion process, and how they correlate with the acquired electrochemical data. Four materials systems, namely AA6061-T6 aluminum alloy, AZ91 magnesium alloy, galvanized DP590 steel, and pure Zn, were investigated under open circuit potential and under potentiodynamic polarization. The multimodal corrosion system was utilized to observe processes such as surface passivation and dissolution, pit and filiform corrosion initiation and propagation, and was correlated with location and magnitude of hydrogen evolution. This approach is shown to yield a truly multimodal understanding of the ongoing corrosion processes.

Investigating electrochemical corrosion at Mg alloy-steel joint interface using scanning electrochemical cell impedance microscopy (SECCIM).

Developing strategies to prevent corrosion at the interface of dissimilar metal alloys is challenging because of the presence of heterogenous distribution of galvanic couples and microstructural features that significantly change the corrosion rate. Devising strategies to mitigate this interfacial corrosion requires quantitative and correlative understanding of its surface electrochemical reaction. In this work, scanning electrochemical cell impedance microscopy (SECCIM) was employed to study location-specific corrosion in the interfacial region of dissimilar alloys, such as AZ31 (magnesium alloy) and DP590 (steel) welded using the Friction-stir Assisted Scribe Technique (FAST) processes. Herein, SECCM and SECCIM were used to perform correlative mapping of the local electrochemical impedance spectroscopic and potentiodynamic polarization to measure the effect of electronic and microstructural changes in the welded interfacial region on corrosion kinetics. Microstructural characterization including scanning electron microscopy and electron backscatter diffraction was performed to correlate changes in microstructural features and chemistry with the corresponding electronic properties that affect corrosion behavior. The variations in corrosion potential, corrosion current density, and electrochemical impedance spectroscopy behavior across the interface provide deeper insights on the interfacial region-which is chemically and microstructurally distinct from both bare AZ31 and DP590 that can help prevent corrosion in dissimilar metal structures.

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model.

Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

A low-cost hierarchical nanostructured beta-titanium alloy with high strength.

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 ß-titanium alloy, Ti-1Al-8V-5Fe (Ti185), by hierarchical nanostructure consisting of homogenous distribution of micron-scale and nanoscale α-phase precipitates within the ß-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 ß-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.