Peak Stir Zone Temperatures during Friction Stir Processing

The stir zone (SZ) temperature cycle was measured during the friction stir processing (FSP) of NiAl bronze plates. The FSP was conducted using a tool design with a smooth concave shoulder and a 12.7-mm step-spiral pin. Temperature sensing was accomplished using sheathed thermocouples embedded in the tool path within the plates, while simultaneous optical pyrometry measurements of surface temperatures were also obtained. Peak SZ temperatures were 990 ⁰Cto 1015 ⁰C (0.90 to 0.97 TMelt) and were not affected by preheating to 400⁰C, although the dwell time above 900 ⁰C was increased by the preheating. Thermocouple data suggested little variation in peak temperature across the SZ, although thermocouples initially located on the advancing sides and at the centerlines of the tool traverses were displaced to the retreating sides, precluding direct assessment of the temperature variation across the SZ. Microstructure-based estimates of local peak SZ temperatures have been made on these and on other similarly processed materials. Altogether, the peak-temperature determinations from these different measurement techniques are in close agreement

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Deep learning models for predicting RNA degradation via dual crowdsourcing

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Processing, deformation and failure in superplastic aluminum alloys: applications of orientation-imaging microscopy

The article of record as published may be found at http://dx.doi.org/10. 1361/10599490421349The importance of grain size refinement in enabling superplasticity is reviewed, and the current understanding of grain boundary characteristics is summarized. The application of orientation-imaging micros- copy (OIM) methods to the processing response and the deformation and failure modes in superplastic aluminum alloys are illustrated through microtexture analysis and determination of grain boundary characteristics in selected commercial materials. Continuous and discontinuous recrystallization reactions exhibit distinct microtextures and grain boundary characteristics. The application of OIM and microtexture analysis to the evaluation of both deformation and failure mechanisms during superplastic forming is illustrated

Microstructural modification of as-cast NiAl bronze by friction stir processing

The application of friction stir processing (FSP) to a cast NiAl bronze (NAB) material is presented as a means for selective modification of the near-surface layers by converting as-cast microstructures to a wrought condition in the absence of macroscopic shape change. This may enable selective surface hardening of cast components. The complex physical metallurgy of the NAB is reviewed, and microstructure changes associated with FSP for a selected set of processing parameters are examined by optical microscopy (OM) and transmission electron microscopy (TEM) methods. Direct temperature measurement in the stir zone is infeasible and, so, these microstructure changes are used to estimate peak temperatures in the stir zone. The persistence of a Fe3Al phase (Kii) indicates that peak temperatures are below the solvus for this phase, while the presence of transformation products of the f3 phase, including fine Widmanstätten a., bainite, and martensite, indicates that peak temperatures exceed the eutectoid temperature for the reaction β →ᵅ + Kiii throughout the stir zone.Defense Advanced Research Projects Agency (DARPA

The influence of friction stir processing parameters on microstructure of as-cast NiAl bronze

The influence of friction stir processing (FSP) parameters on the evolution of microstructure in an equilibrium-cooled, as-cast NiAl bronze (NAB) material was evaluated by optical microscopy (OM) and transmission electron microscopy (TEM) methods. A threaded pin tool was employed and tool rotation and traversing rates were varied in order to examine the spatial variation of stir zone microstructures in relation to FSP parameters. For processing at low rotation and traversing rates, the microstructure throughout the stir zone consists of elongated and banded grains of the primary a and transformation products of the 1 phase. Such microstructures reflect severe deformation at temperatures up to �900 °C in the a + 1 two-phase region for this NAB material. Increasing rotation and traversing rates, coarse Widmanstätten a near the surface in contact with the tool became apparent. The appearance of this constituent reflects nearly complete transformation to 1 during FSP with peak temperatures of �1000 °C. Also, complex stir zone flow patterns, often referred to as onion ring structures, become distinct in the mid regions of the stir zones as rotation and traversing rates increase. Schematic representations illustrating the effect of FSP parameters on thermal cycles at various locations in stir zones were prepared based on microstructure observations. Thus, processing at higher rotation and traversing rates results in higher peak temperatures near the surface in contact with the tool but also in steeper temperature gradients when compared to lower rotation and traversing rates.Defense Advanced Research Projects Agency (DARPA

Microstructural Control of a Precipitate-Hardenable Al-Ag Alloy Using Severe Plastic Deformation

An Al-10.8wt%Ag alloy was subjected to aging treatment followed by Equal-Channel Angular Pressing (ECAP) (designated process AE) or ECAP followed by aging treatment (designated process EA). Hardness measurements were undertaken with respect to the number of ECAP passes for process AE or with respect to aging time for process EA. Microstructures were examined by transmission electron microscopy (TEM) including X-ray mapping. It is shown that age hardening is observed for the ECAP sample due to the precipitation of very fine particles within the small grains

Grain boundary structure in Al-Mg and Al-Mg-Sc alloys after equal-channel pressing

Samples of an Al-3% Mg alloy and an Al-3% Mg-0.2% Sc alloy were subjected to equal-channel angular pressing (ECAP) to reduce the grain size to approximately 0.2-0.3 ìm. Some samples of each alloy were also annealed for 1 h at temperatures of either 423 or 673 K, respectively. High-resolution electron microscopy was used to examine the microstructure both before and after annealing. The grain boundaries after ECAP were wavy and faceted and in high-energy nonequilibrium configurations. These results were consistent with earlier observations of materials subjected to severe plastic deformation using high-pressure torsion. In addition, some grain boundaries in the Al-Mg-Sc alloy had a zigzag appearance after annealing at 673 K, where the straight portions of the boundary were identified as low-energy {111} planes. It is suggested these are mobile boundaries lying in a lowest energy configuration where mobility may be restricted by the presence of incoherent Al3Sc particles