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Development of a piezo stack – laser doppler vibrometer sensing approach for characterizing shear wave dispersion and local viscoelastic property distributions
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Microstructure and Mechanical Properties of the AA7075 Tube Fabricated using Shear Assisted Processing and Extrusion (ShAPE)
Shear Assisted Processing and Extrusion (ShAPE) experimental setup and tooling were adopted for extruding thin-walled AA7075 aluminum tube from as-cast non-homogenized billet material in a single run. The mechanical and microstructural characterizations were performed on the extrudate in the as-extruded condition without subsequent heat treating. It was shown that the ShAPE process developed a significantly refined microstructure with uniform and almost equiaxed grain structure on both hoop and axial cross-sections of the extrudate as well as through the thickness of the material. The pole figures and inverse pole figures of the EBSD data showed an strong shear texture development, and it was found out that axial shear is the dominant deformation mechanism in the regions near the inner surface of the tube, while combined axial and torsional shears are the two dominant modes of deformation near the outer surface of the extrudate. As for the mechanical properties, there was an increase of 150% and 73% in the yield and ultimate strengths of the tube produced using ShAPE process, respectively, and a 18% decrease in maximum uniform plastic elongation compared to the conventionally extruded AA7075-O tube.
Effect of post-extrusion heat treatment on mechanical property of aluminum alloy 2024 tube produced using Shear Assisted Processing and Extrusion (ShAPE)
The present work deals with the Shear Assisted Processing and Extrusion (ShAPE) of aluminum alloy 2024 tubes and mechanical property optimization via post extrusion heat treatments (T3510 and T8510). The ShAPE process enables extruding AA2024 at an extrusion speed of 7.4 m/min and extrusion temperature of 480 °C, which is otherwise beyond the extrudability limit of this alloy using conventional extrusion techniques. The ultimate tensile strength reaches 466 MPa for ShAPE+T3510 condition and 503 MPa for ShAPE+T8510 condition, both of which exceed the corresponding ASTM/ASM (minimum/typical) values for the studied tube geometry. The yield strength reaches 382 MPa for ShAPE+T3510 condition and 481 MPa for ShAPE+T8510 condition, both of which surpass any reported values in the literature or the highest industrial values. In addition, the ductility of AA2024 tube fabricated from ShAPE+T3510 or ShAPE+T8510 are evidently improved by the grain size refinement, second phase refinement, and enhanced uniformity in second phase distribution due to the severe plastic deformation of ShAPE process.
THE ROLE OF FRACTURE PROPERTIES ON LAP JOINT STRENGTH OF FRICTION STIR WELDED AA7055-T6 SHEETS
Friction stir lap welded (FSLW) joints have weight-saving potential in aluminum-intensive automotive assembly. However, FSLW also modifies the material microstructure close to the joint. Optimizing the FSLW process requires understanding the relationship between the strength and the joint's microstructure. In previous studies, efforts have been dedicated to determining the effects of local softening, the shape of the oxide line, and porosity. However, the impact of changes to the fracture properties on the joint's strength has not been studied. In this work, strength testing, and simulations, aided by material characterization, were used to determine the role of fracture properties on the shear strength of a 3-sheet (aluminum alloys 7055-7055-6022) lap joint. Characterization involved testing for fracture properties in the weld region. This data was then implemented into finite element simulations. As a result, the joint strength was predicted with a deviation of less than 6% from the experimental value. Comparison with strength prediction using only the base metal properties indicates that fracture property in the nugget region determines the strength of AA7055 FSLW.
Multi-Objective design of interlocking metasurfaces using conditional diffusion models
Unit cell design remains a major challenge for interlocking metasurfaces, a promising joining technology for dissimilar materials, due to the complex, competing, multivariate design space and the need for rapid adaptation to varying performance requirements. This study explores Conditional Diffusion Models as a design optimization tool for interlocking metasurfaces. Given the complex, competing, multivariate design space for interlocking metasurfaces, unit cell design remains a major challenge for this joining technology. We trained a conditional diffusion model on 25,000 finite element analysis-simulated interlocking metasurface unit cells to generate designs with tailored thermo-mechanical properties (tensile strength, shear strength, and thermal conductivity) based on specified performance criteria. The model demonstrated a success rate of approximately 72 % in producing designs that met specified property bounds. The conditional diffusion model generated both thermally resistive and conductive designs, revealing clear trends in design characteristics: taller, dendritic structures were advantageous for tensile loads, while shorter, robust designs excelled in shear applications. Our findings indicate that the model's performance is more influenced by the breadth of the design space than by the quantity of training data, highlighting the importance of expansive design domains for generating innovative solutions. This work establishes conditional diffusion models as a highly efficient and adaptable tool for rapid interlocking metasurface unit cell design, paving the way for advancements in multi-material joining technologies, as well as highlighting the justification to leverage conditional diffusion models as design tools across complex design domains.
Energetics of the nucleation and glide of disconnection modes in symmetric tilt grain boundaries
Grain boundaries (GBs) evolve by the nucleation and glide of disconnections, which are dislocations with a step character. In this work, motivated by recent success in predicting GB properties such as the shear coupling factor and mobility from the intrinsic properties of disconnections, we develop a systematic method to calculate the energy barriers for the nucleation and glide of individual disconnection modes under arbitrary driving forces and a quasi-2D setting. This method combines tools from bicrystallography to enumerate disconnection modes and the Nudged elastic band (NEB) method to calculate their energetics, yielding minimum energy paths and atomistic mechanisms for the nucleation and glide of each disconnection mode. We apply the method to accurately predict shear coupling factors of $[001]$ symmetric tilt grain boundaries in Cu. Particular attention is paid to the boundaries where the dislocation-based disconnection nucleation model produces incorrect nucleation barriers. We demonstrate that the method can accurately compute energy barriers and predict shear-coupling factors in the low-temperature regime. For certain disconnection modes in which the assumptions underlying our method do not hold, we report upper bounds on the energy barriers for disconnection nucleation and glide. In addition, the NEB trajectories reveal interesting phenomena such as the dissociation of a higher energy mode into lower energy modes, and in some cases, shear coupling being mediated by partial disconnections, wherein the GB structure temporarily changes to a metastable state before reverting back to its original structure. Graphical abstract
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their development in the lab. This means that material and process development proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), an emerging solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
Melt Processing Pretreatment Effects on Enzymatic Depolymerization of Poly(ethylene terephthalate)
Poly(ethylene terephthalate) (PET) is a common thermoplastic material, used in a wide variety of applications (i.e., bottles, fabrics, packaging, electronics, and automotive components). Increasing demand for PET has precipitated a need for improved recycling technology, especially for single-use PET waste. Recently, enzymatic depolymerization has shown promise as an environmentally responsible alternative for PET chemical recycling that yields economically useful products (e.g., terephthalic acid, adipic acid, and ethylene glycol). However, the depolymerization system still suffers from low rates on crystalline PET substrates, and effects of realistic waste streams are not known. In our work, PET waste is pretreated using an ultra-high-speed twin-screw extruder system. PET substrates were modified by various processing pretreatments to allow enzymes better access to depolymerize substrate materials. The effect of varying throughput and mechanical shear on structural properties of the PET waste was analyzed using molecular weight and thermal characterizations. These pretreated samples exhibit modifications in molecular weight, glass transition temperature, crystallinity, and specific surface area. The unpurified leaf-branch compost cutinase enzyme produced from the fed-batch fermentation of Escherichia coli BL21(DE3) was used in enzymatic depolymerization, where a faster reaction was observed as crystallinity was decreased and the specific surface area was increased. The rate of terephthalic acid production was also significantly higher for samples processed at lower mechanical shear with higher throughputs. As a result, this work demonstrates the potential for tailoring pretreatments in pursuit of faster and more energy efficient PET recycling using enzymes, with facile adaptation to the industrial scale for the circular economy.
Joint influence of γ–irradiation and high temperature shear grinding on the IR spectra and surface–energy properties of polyethylene
The optical and surface-energy properties of polyethylene (PE) subjected to γ-irradiation and post-radiation, high temperature, shear grinding are presented. Comparison of the IR spectra of the initial and γ-irradiated PE shows that oxygen-containing groups and unsaturated bonds in the polymer macromolecules are formed and accumulate during their radiolysis in air, increasing with γ-irradiation dose. A redistribution of the products of radiation-chemical conversion on the surface of the polymer granules throughout the entire volume of the powder occurs after high-temperature shear milling to a powder as observed in the IR spectrum. Polar functional groups due to oxidation reactions are observed on the surface layer of the sample after post-radiation grinding. Furthermore, the initial polymer surface is weakly basic with an acidity parameter of -0.34. The value of the acidity parameter of the sample made of ground irradiated PE has an acidic surface with an acidity parameter of 0.65. γ irradiation of the secondary processing of PE waste with high-temperature shear grinding is a promising approach to obtain a secondary PE powder with specified parameters and different degrees of functionalization.
EGS Collab Experiment 2: 4100 Foot Level Elastic Property Data
Static and dynamic elastic properties (Young's modulus, Shear Modulus, P-wave Modulus, and Poisson's Ratio) of amphibolites and rhyolites from the TV4100 and TH4100 boreholes at the Sanford Underground Research Laboratory (SURF). Elastic properties include Young's modulus, Poisson's ratio, shear modulus, and p-wave modulus. Raw data from the experiments and slides describing the experimental procedure and a summary of results are included, along with a readme file with additional definitions and information.
Lattice misorientation evolution and grain refinement in Al-Si alloys under high-strain shear deformation
The starting microstructure of an alloy can be tailored to achieve varying degrees of microstructural refinement and enhance mechanical properties through severe plastic shear deformation during solid-phase processing. Crystal plasticity-based grain misorientation modeling, coupled with systematic pin-on-disk tribometry-based subsurface shear deformation experiments on as-cast Al-xSi alloys (x = 0, 1, 4 at %), was conducted. The post-deformation microstructural analysis, through a combined computational and experimental approach, conclusively shows that the initial volume fraction of the hard Si phase enhances the evolution of local lattice misorientation, leading to efficient grain refinement during severe plastic shear deformation. The shear–deformation-induced nanostructure resulted in more than double the nanoindentation hardness in the processed alloy.
Microstructural evolution in shear-punch tests: A comparative study of pure Cu and Cu-Cr alloy
Understanding the mechanisms behind microstructural evolution during shear deformation has been a long-standing area of interest. However, establishing a connection between microstructure, mechanical properties, and extent of shear deformation is challenging and requires refined experimental approaches. Shear-punch testing (SPT) provides a controlled method to introduce shear into small volumes of material that later can be subjected to detailed microstructural characterization. In this study, we utilize an SPT device to induce shear deformation to pure copper and a binary copper-chromium alloy. Electron backscatter diffraction and transmission electron microscopy were used to study the mechanisms of plastic deformation after SPT. Our results indicate that shear deformation of pure Cu produces a dense network of intercepting microshear bands upon sustained deformation. Twin boundaries undergo degradation into high angle grain boundaries due to simultaneous deviation from the axis-angle pair condition of 60° misorientation on [111] direction. The presence of 50% volume Cr particles in the soft Cu matrix fundamentally altered the shear deformation mechanism. Preferential deformation of the Cu matrix led to accelerated shear-induced formation of low and high angle grain boundaries, and subsequent grain refinement. Comparatively, no grain refinement occurred in pure Cu at a strain ~10 times larger (ϵ = 4.73) than that of the copper-chromium case (ϵ = 0.42). Overall, our study sheds light on the microstructural evolution during shear deformation and highlights the significant influence of particle reinforcements on the shear deformation mechanisms of metals.
Mode spectrum characteristics and onset of the low-shear MHD stability regime
Equilibria with extended regions of weak magnetic shear, including some tokamak scenarios and stellarators, can be susceptible to pressure-driven internal MHD instabilities even though there is no mode rational surface in the plasma. Nonresonant modes, in particular, can have properties that are unattractive for confinement, including displacing substantial volumes of the plasma and leading to more efficient pressure gradient flattening in the nonlinear regime. The onset and linear properties of the low shear stability regime are examined using the initial-value, extended-MHD code M3D-C. For monotonic q-profiles, we demonstrate a clear correlation between the convergents associated with the continued fraction representation of q 0 and the spectrum of unstable modes. Nonresonant modes are observed to be destabilized preferentially to any other resonant instability with the same toroidal mode number when n > 1. Using the observed connection between the spectrum associated with q 0 and the overall equilibrium stability properties, we suggest a technique for reducing the uncertainty on both q 0 and magnetic shear in the core region, obtained either via measurement or through the analysis and reconstruction of experimental results.
Near-Cloud Atmospheric Ingredients for Deep Convection Initiation
A lack of routine environmental observations located near deepening cumulus congestus clouds limits verification of important theorized and simulated updraft–environment interaction processes occurring during deep convection initiation (CI). We analyze radiosonde profiles collected during several hundred CI events near a mountain range in central Argentina during the CACTI field campaign. Statistical analyses illustrate environmental conditions supporting radar-observed CI outcomes that span a spectrum of convective cell depths, widths, and durations, as well as events lacking precipitating convection. Tested environmental factors include a large variety of sounding-derived measurements of CAPE, CIN, moisture, terrain-relative winds, vertical shear, and lifted parcel properties, with supplemental model reanalysis of background larger-scale vertical motion. CAPE and CIN metrics do not consistently differentiate CI success from failure. Only a few environmental factors contain consistent monotonic relationships among the spectrum of cloud depths achieved during CI: (i) the depth and strength of background ascent, and (ii) the component of low-level flow oriented parallel to the ridgeline. These metrics suggest that the ability of the surrounding flow to lift parcels to their LFC and terrain-modified flow are consistently relevant processes for CI. Low- to midlevel relative humidity strongly discriminated between CI and non-CI events, likely reflecting entrainment-driven dilution processes. However, we could not confidently conclude that relative humidity similarly discriminated robust from marginal CI events. Circumstantial evidence was found linking cell width, an important cloud property governing the probability of CI, to LCL height, boundary layer depth, depth and magnitude of the CIN layer, and ambient wind shear.
Shear wave generation by mode conversion in picosecond ultrasonics: Impact of grain orientation and material properties
Abstract Picosecond ultrasonics has seen wide application in the investigation of interfaces in semiconductors and 3D imaging in biological systems. Shear ultrasonic waves are important for elastic constant and grain orientation measurement in ceramic materials. In this study, we investigate the impact of grain orientation and material's elastic properties on the generation efficiency of shear waves by mode conversion at a transducer thin film/substrate interface. The solution of acoustic wave equations suggests that crystal grain orientation has a strong impact on the generated shear wave amplitude. This dependence is found to be closely related to the magnitude of longitudinal component of wave displacements. The applicability of analytical model is validated by the experimental results from time‐domain Brillouin scattering. Moreover, material properties determine acoustic wave amplitudes based on the acoustic mismatch model and, particularly, large elastic anisotropy defined by Zener ratio favors strong shear waves. In the light of this analysis, several recommendations on suitable grain orientations and film materials are made to facilitate shear wave detection.
Let’s Stick Together: Interplay of Moisture, Particle Size, and Anatomical Fraction in the Flowability of Corn Stover Crumbles
Handling of agricultural waste biomass feedstocks (such as corn stover) is a persistent challenge in the production of biofuels and chemicals at integrated biorefineries. Inconsistent flowability and high feedstock variability creates equipment downtime and processing challenges. This work investigates the influence of anatomical fractionation and moisture content on the bulk solids handling behavior of corn stover crumbles. A combination of rotational shear testing, tribological measurements, and surface and sorption characterization techniques (inverse gas chromatography (IGC), dynamic vapor sorption (DVS), and electron microscopy) were used to evaluate the contributions of particle composition, size, and surface properties to flow behavior. Shear and frictional testing revealed that moisture content has the greatest effect on flowability, significantly increasing unconfined yield strength (ƒ c ) and reducing the flow function coefficient (F F C), regardless of anatomical fraction. While differences in surface energy were observed between fractions, particularly in polar contributions (γ AB ), these did not correspond to meaningful differences in flow behavior. Coarser 6 mm particles exhibited poorer flow performance than 4 mm crumbles, likely due to broader particle size distributions and increased particle interlocking. Taken together, these results suggest that anatomical fractionation provides minimal benefit from a bulk handling perspective. Moisture content and particle size heterogeneity dominate handling behavior, reinforcing the utility of unfractionated corn stover in processing environments. These insights can inform the design of more efficient and reliable feedstock processing systems for agricultural biomass in integrated biorefineries.
Influence of sizing concentration on strength, stiffness, and porosity in textile grade carbon fiber (TCF)-Epoxy composites: Revealing inverse trends
The effect of fiber sizing (i.e., surface treatment) concentration (0 %, 1.36 %, 1.52 %, 1.94 %, and 2.13 %) on the mechanical properties (tensile, flexural, interlaminar shear strength (ILSS), and low velocity impact) of textile grade carbon fiber (TCF)-epoxy composite is examined. An inverse relationship between the strength and stiffness of the composite is observed with increased sizing concentration. The root mean square (RMS) roughness of the fiber surface increased from 17.8 nm (unsized) to 22.7 nm with 2.13 % sizing concentration. It was found that the tensile strength increased by 131 % from 221.4 ± 18.5 MPa (unsized) to 510.8 ± 28.05 MPa (for 1.36 % sizing) and further by 155 %–563.7 ± 14.95 MPa at 2.13 % sizing. On the contrary, the stiffness is initially increased by 126 % from 33.52 ± 7.80 GPa (unsized) to 75.9 ± 3.21 GPa (for 1.36 % sizing) but reduced with further increase in the sizing concentration. A single fiber pull-out test is simulated using the finite element method to validate the reverse trend in strength and stiffness. The varying sizing concentration is simulated by introducing an interface of varying thickness between fibers and matrix. Simulation results confirm that a thicker interface, corresponding to a higher sizing concentration, decreases interfacial shear stress, enhancing material strength while decreasing stiffness. The reverse trend in strength and stiffness with the sizing concentration aligns with experimental observations. In conclusion, the present study emphasizes the importance of sizing concentration for mechanical properties and provide a design criterion for customized high-strength and high-stiffness applications.