Search

The tribological performance and underlying deformation behavior of Ni55Ti45, Ni54Ti45Hf1 and Ni56Ti36Hf8 alloys were studied using rolling contact fatigue (RCF) testing and transmission electron microscopy (TEM). TEM results of the as-machined RCF rods, prepared using focus ion beam, revealed some damage very close to the surface. TEM results after initial RCF cycling showed that additional damage was mainly confined to deformation bands that propagated several microns into the sample. These bands formed via localized dislocation slip, possibly on multiple slip systems, within the B2 matrix and/or within transformed B19 prime martensite phase under repeated applied contact stress. Further cycling of Ni55Ti45 and Ni54Ti45Hf1 led to shearing and dissolution of the strengthening precipitates within the deformation bands, followed by formation of nanocrystalline grains and finally amorphization of the remaining matrix material within the bands. The Ni56Ti36Hf8 alloy exhibited a notable increase in RCF performance and a smaller damage zone (1.5 microns) compared to the Ni55Ti45 and Ni54Ti45Hf1 alloys (over 6 microns). This was attributed to the low fraction of B2 matrix phase (less than or equal to 13 %) in the Ni56Ti36Hf8 alloy, leading to formation of narrow deformation bands (less than 11 nm) that were incapable of dissolving the much larger precipitates. Instead, the deformation bands were restricted to narrow channels between the dense cubic NiTiHf and H-phase precipitates. In contrast, broad deformation bands accompanied by shearing and eventual dissolution of the Ni4Ti3 precipitates were observed in the Ni55Ti45 and Ni54Ti45Hf1 alloys due to the high area fractions of B2 matrix phase (~49 %).

The effects of various heat treatments on the microstructure and hardness of new Ni56Ti41Hf3 and Ni56Ti36Hf8 (atomic %) alloys were studied to evaluate the suitability of these materials for tribological applications. A solid-solution strengthening effect due to Hf atoms was observed for the solution annealed (SA) Ni56Ti36Hf8 alloy (716 HV), resulting in a comparable hardness to the Ni56Ti41Hf3 alloy containing 54 vol.% of Ni4Ti3 precipitates (707 HV). In the Ni56Ti41Hf3 alloy, the maximum hardness (752 HV), achieved after aging at 300C for 12 h, was attributed to dense, semi-coherent precipitation of the Ni4Ti3 phase. Unlike the lenticular morphology usually observed within binary NiTi alloys, a blocky Ni4Ti3 morphology formed within Ni56Ti36Hf3 due to a smaller lattice mismatch in the direction normal to the habit plane at the precipitate/matrix interface. The maximum hardness for Ni56Ti36Hf8 (769 HV) was obtained after applying an intermediate aging step (300C for 12 h) followed by normal aging (550C for 4 h). This two-step aging treatment induces dense nanoscale precipitation of two interspersed precipitate phases, namely H-phase and a new cubic Ni-rich precipitate phase, resulting in the highest hardness exhibited yet by this family alloys. The composition of cubic Ni-rich precipitates was measured using atom probe tomography to be approximately Ni61.5Ti31Hf7.5, while HAADF-STEM revealed a 54 atom motif cubic structure (a= 8.87 Angstroms), and electron diffraction showed that the structure belongs to the pm-3m (No. 221) space group.

Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A physics-informed featured engineering approach is shown to enable otherwise poorly performing ML models to perform well with the same data. Specifically, previously engineered elemental features based on alloy chemistries are combined with newly engineered heat treatment process features. The new features result from first transforming the heat treatment parameter data as it was previously recorded using nonlinear mathematical relationships known to describe the thermodynamics and kinetics of phase transformations in alloys. The ability of the ML model to be used for predictive design is validated using blind predictions. Composition - process - property relationships for thermal hysteresis of shape memory alloys (SMAs) with complex microstructures created via multiple melting-homogenization-solutionization-precipitation processing stage variations are captured, in addition to the mean transformation temperatures of the SMAs. The quantitative models of hysteresis exhibited by such highly processed alloys demonstrate the ability for ML models to design for physical complexities that have challenged physics-based modeling approaches for decades., Comment: Submitted to Journal, 34 pages, 6 main figures/tables, and 9 supplementary figures/tables

Dislocation structures, chemical segregation, {\gamma ^{\prime}, {\gamma ^{\prime \prime}}, {\delta} precipitates and Laves phase were quantified within the microstructures of Inconel 718 (IN718) produced by laser powder bed fusion additive manufacturing (AM) and subjected to standard, direct aging, and modified multi-step heat treatments. Additionally, heat-treated samples still attached to the build plates vs. those removed were also documented for a standard heat treatment. The effects of the different resulting microstructures on room temperature strengths and elongations to failure is revealed. Knowledge derived from these process structure property relationships was used to engineer a super solvus solution anneal at 1020 degC for 15 minutes, followed by aging at 720 degC for 24 hours heat treatment for AM-IN718 that eliminates Laves and {\delta} phases, preserves AM specific dislocation cells that are shown to be stabilized by MC carbide particles, and precipitates dense {\gamma ^{\prime} and {\gamma ^{\prime \prime}} nanoparticle populations. This 'optimized for AM-IN718 heat treatment' results in superior properties relative to wrought/additively manufactured, then industry standard heat treated IN718: relative increases of 7/10 percent in yield strength, 2/7 percent in ultimate strength, and 23/57 percent in elongation to failure are realized, respectively, regardless of as-built vs. machined surface finishes., Comment: After peer-review and minor revisions

Elastocaloric cooling, which exploits the latent heat released and absorbed as stress-induced phase transformations are reversibly cycled in shape memory alloys, has recently emerged as a frontrunner in non-vapor-compression cooling technologies. The intrinsically high thermodynamic efficiency of elastocaloric materials is limited only by work hysteresis. Here, we report on creating high-performance low-hysteresis elastocaloric cooling materials via additive manufacturing of Titanium-Nickel (Ti-Ni) alloys. Contrary to established knowledge of the physical metallurgy of Ti-Ni alloys, intermetallic phases are found to be beneficial to elastocaloric performances when they are combined with the binary Ti-Ni compound in nanocomposite configurations. The resulting microstructure gives rise to quasi-linear stress-strain behaviors with extremely small hysteresis, leading to enhancement in the materials efficiency by a factor of five. Furthermore, despite being composed of more than 50% intermetallic phases, the reversible, repeatable elastocaloric performance of this material is shown to be stable over one million cycles. This result opens the door for direct implementation of additive manufacturing to elastocaloric cooling systems where versatile design strategy enables both topology optimization of heat exchangers as well as unique microstructural control of metallic refrigerants., Comment: 38 pages, 3 figures, 8 supplementary figures, 1 supplementary table

Near-field and far-field high-energy diffraction microscopy and microcomputed tomography X-ray techniques were used to study a bulk single crystal nickel-titanium shape memory alloy sample subjected to thermal cycling under a constant applied load. Three-dimensional in situ reconstructions of the austenite microstructure are presented, including the structure and distribution of emergent grain boundaries. After one cycle, the subgrain structure is significantly refined, and heterogeneous {\Sigma}3 and {\Sigma}9 grain boundaries emerge. The low volume and uneven dispersion of the emergent {\Sigma} boundaries across the volume show why previous transmission electron microscopy investigations of {\Sigma} grain boundary formation were inconsistent., Comment: accepted in Scripta Materialia

Three-dimensional (3D) X-ray diffraction methods were used to analyze the evolution of the load-induced rearrangements of monoclinic twin microstructures within bulk nickel-titanium specimens in 3D and across six orders of magnitude in length scales: changes in lattice plane spacings and orientations at the nanoscale, growth and nucleation of martensite twin variants at the microscale, and localization of plastic strain into deformation bands at the macroscale. Portions of the localized deformation bands were reconstructed in situ and in 3D. Analyses of the data elucidate the sequence of twin rearrangement mechanisms that occur within the propagating localized deformation bands, connect these mechanisms to the texture evolution, and reveal the effects of geometrically necessary lattice curvature across the band interfaces. The similarities between shear bands and localized deformation bands in twin reorientation are also discussed. These findings will guide future researchers in employing twin rearrangement in novel multiferroic technologies, and they demonstrate the strength of 3D, multiscale, in situ experiments to improve our understanding of complicated material behaviors and to provide opportunities to advance our abilities to model them., Comment: accepted in Journal of the Mechanics and Physics of Solids

Using a combination of numerical simulations and atom-probe tomography experiments, we determine the interfacial energy of Cu nanocrystals precipitated within the amorphous matrix of FINEMET (molar composition Fe72.89Si16.21B6.90Nb3Cu1). Specifically, we use the Langer-Schwartz model implemented in the software Thermocalc to carry out parametric simulations of growth and coarsening of Cu clusters for different interface energies. We have carried out atom-probe tomography (APT) experiments to determine the interface energy as the value for which the simulated particle size distribution best matches the experimental data. This combination of APT and precipitation modeling can be applied to other nanocrystals precipitated within amorphous matrices.

We aim to investigate relationships between select processing parameters or inputs (composition, temperature, annealing time) and two structural parameters, specifically, the mean radius and volume fraction of the Fe$_3$Si nanocrystals. To this end, we have deviced a combined CALPHAD and machine learning approach that led to well-calibrated metamodels able to predict structural parameters quickly and accurately for any desired inputs. In order to generate data for the mean radius and volume fraction of Fe$_3$Si nanocrystals, we have used a precipitation model based in the software Thermocalc to perform annealing simulations at a set of temperatures (490-550~\degree C) and for varying Fe and Si concentrations (Fe$_{72.89 +x}$Si$_{16.21-x}$B$_{6.90}$Nb$_{3}$Cu$_{1}$, $-3\leq x \leq 3$ atomic \%). Thereafter, we used the data to develop metamodels for the mean radius and volume fraction via the \emph{k}-Nearest Neighbour algorithm. The metamodels are shown to reproduce closely the trends obtained from the precipitation model over the entire annealing timescale. Our further analysis via parallel coordinate charts shows the effect of composition, temperature, and annealing time, and helps identify combinations thereof that lead to the desired mean radius and volume fraction for the nanocrystalline phase. This approach utilizes experimental (thermodynamic and kinetic) databases from the CALPHAD approach so as to capture the physics of nucleation and growth, while the machine learning algorithm provides the robustness needed to analyze the effects of processing parameters for this complex precipitation problem. This work contributes to understanding the linkages between processing parameters and desired microstructural characteristics (crystal size and volume fraction) responsible for achieving targeted properties, and illustrates ways to reduce the time from alloy discovery to deployment., Comment: 10 pages, 15 figures

Research over the years has shown that the formation of the Fe$_3$Si phase in FINEMET (Fe-Si-Nb-B-Cu) alloys leads to superior soft magnetic properties. In this work, we use a CALPHAD approach to derive Fe-Si phase diagrams to identify the composition-temperature domain where the Fe$_3$Si phase can be stabilized. Thereafter, we have developed a precipitation model capable of simulating the nucleation and growth of Fe$_3$Si nanocrystals via Langer-Schwartz theory. For optimum magnetic properties, prior work suggests that it is desirable to precipitate Fe$_3$Si nanocrystals with 10-15 nm diameter and with the crystalline volume fraction of about 70 \%. Based on our parameterized model, we simulated the nucleation and growth of Fe$_3$Si nanocrystals by isothermal annealing of Fe$_{72.89}$Si$_{16.21}$B$_{6.90}$Nb$_{3}$Cu$_{1}$ (composition in atomic \%). In numerical experiments, the alloys were annealed at a series of temperatures from 490 to 550 \degree C for two hours to study the effect of holding time on mean radius, volume fraction, size distribution, nucleation rate, number density, and driving force for the growth of Fe$_3$Si nanocrystals. With increasing annealing temperature, the mean radius of Fe$_3$Si nanocrystals increases, while the volume fraction decreases. We have also studied the effect of composition variations on the nucleation and growth of Fe$_3$Si nanocrystals. As Fe content decreases, it is possible to achieve the desired mean radius and volume fraction within one hour holding time. The CALPHAD approach presented here can provide efficient exploration of the nanocrystalline morphology for most FINEMET systems, for cases in which the optimization of one or more material properties or process variables are desired., Comment: Submitted to Journal of Alloys and Compounds

Deformation heterogeneities within microstructures of polycrystalline shape memory alloys (SMAs) during superelastic stressing are studied using both experiments and simulations. In situ X-ray diffraction, specifically the far-field high energy diffraction microscopy (ff-HEDM) technique was used to non-destructively measure the grain-averaged statistics of position, crystal orientation, elastic strain tensor and volume for hundreds of austenite grains in a superelastically loaded nickel-titanium (NiTi) SMA. This experimental data were also used to create a synthetic microstructure within a finite element model. The development of intragranular stresses were then simulated during tensile loading of the model using anisotropic elasticity. Driving forces for phase transformation and slip were calculated from these stresses. The grain-average responses of individual austenite crystals examined before and after multiple stress-induced transformation events showed that grains at the specimen interior carry more stress and plastically deform less than the surface grains as the superelastic response shakes down. Examination of the heterogeneity within individual grains showed that regions near grain boundaries exhibit larger stress variation compared to the grain interiors. This intragranular heterogeneity is more strongly driven by the constraints of neighboring grains than the initial stress state and orientation of the individual grains.