Microscale residual stresses in additively manufactured stainless steel
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Biodegradable pectin polymers have been recommended for a variety of biomedical applications, ranging from the delivery of oral drugs to the repair of injured visceral organs. A promising approach to regulate pectin biostability is the blending of pectin films. To investigate the development of conjoined films, we examined the physical properties of high-methoxyl pectin polymer-polymer (homopolymer) interactions at the adhesive interface. Pectin polymers were tested in glass phase (10–13% w/w water content) and gel phase (38–41% w/w water content). The tensile strength of polymer-polymer adhesion was measured after variable development time and compressive force. Regardless of pretest parameters, the adhesive strength of two glass phase films was negligible. In contrast, adhesion testing of two gel phase films resulted in significant tensile adhesion strength (p < 0.01). Adhesion was also observed between glass phase and gel phase films—likely reflecting the diffusion of water from the gel phase to the glass phase films. In studies of the interaction between two gel phase films, the polymer-polymer adhesive strength increased linearly with increasing compressive force (range 10–80 N) (R 2 = 0.956). In contrast, adhesive strength increased logarithmically with time (range 10–10,000 s) (R 2 = 0.913); most of the adhesive strength was observed within minutes of contact. Fracture mechanics demonstrated that the adhesion of two gel phase films resulted in a conjoined film with distinctive physical properties including increased extensibility, decreased stiffness and a 30% increase in the work of cohesion relative to native polymers (p < 0.01). Scanning electron microscopy of the conjoined films demonstrated cross-grain adhesion at the interface between the adhesive homopolymers. These structural and functional data suggest that blended pectin films have emergent physical properties resulting from the cross-grain intermingling of interfacial pectin chains.
The ASTM International Standard Test Method for Tensile Stress-Strain of Carbon and Graphite (ASTM C749) requires dog-bone shaped specimens. Unfortunately, specimen sizes required in ASTM C749 are much too large for irradiation within a test reactor and are incompatible with irradiation capsule volumes, molten salt degradation facilities, or oxidation apparatus. The ASTM International Standard Test Method for Tensile Strength Estimate by Disc Compression of Manufactured Graphite (ASTM D8289) was developed as an alternative that provides a means for testing tensile properties using smaller specimens when geometric constraints are present. However, the important stress–strain relation throughout the test loading history that could be recorded using ASTM C749 is lost by using ASTM D8289 because of the difficulties involved with using traditional strain gauges or extensometers on such a small specimen. In this work, a digital image correlation (DIC) method, which uses a full-field noncontact surface displacement measurement technique, was applied along with the ASTM D8289 splitting tensile test on eight small samples of graphite grade Mersen 2114 to track their deformation during compressive loading. Initial results showed that the DIC technique can measure the surface displacement/strain on these small (Ø6 × 3 mm) graphite specimens with high accuracy and good repeatability, indicating that the DIC technique, combined with the small-disc splitting tensile test in ASTM D8289, can capture the strain evolution in the test. In conclusion, further comparison of measured strains with analytical and numerical simulation results provides additional understanding of the mechanical behaviour of this nuclear graphite material in complex stress states.
Excellent mechanical properties of nanomaterials are usually attributed to their nanoscale feature size and high-density interfaces. This work explores the effects of intrinsic interface properties on mechanical properties of nano metallic laminates (NMLs). We show that the interface strength significantly affects the mechanical properties of NMLs. Via in situ mechanical testing and characterization, we investigate the deformation behavior and concurrent microstructure evolution in NMLs with four types of dissimilar interfaces. Our results show that the strength and deformation behaviors of NMLs are largely determined by interface strength. Strong interfaces can simultaneously increase the strength, work hardening capability, and deformability of NMLs. In conclusion, this work brings new insight into the design of advanced metallic materials through tuning of interface properties.
Composite materials possess a high strength-to-weight ratio. A key determinant of their mechanical performance is the interfacial strength between the fibers and the matrix. Sizing agents are commonly used to improve this interface by promoting better adhesion, though optimizing this interaction remains a significant challenge. Here, this study evaluates the use of poly(catechol-styrene) (PCS), a mussel-inspired sizing agent, to enhance fiber–matrix bonding in carbon fiber composites. Woven carbon fiber laminates were dip-coated with varying concentrations of PCS (0.05 and 0.1 wt%) and subsequently fabricated using vacuum-assisted resin transfer molding followed by compression molding. Interlaminar shear strength (ILSS) tests showed improvements of 4% and 8% for the 0.05% and 0.1% PCS treatments, respectively. These results indicate that PCS is effective in reinforcing interfacial adhesion, thereby improving the mechanical integrity of carbon fiber-reinforced composites.
We study the high-pressure strength of Pb and Pb-4wt%Sb at the National Ignition Facility. We measure Rayleigh-Taylor growth of preformed ripples ramp compressed to ~400 GPa peak pressure, among the highest-pressure strength measurements ever reported on any platform. We find agreement with 2D simulations using the Improved Steinberg-Guinan strength model for body-centered-cubic Pb; the Pb-4wt%Sb alloy behaves similarly within the error bars. Here, the combination of high-rate, pressure-induced hardening and polymorphism yield an average inferred flow stress of ~3.8 GPa at high pressure, a ~250-fold increase, changing Pb from soft to extremely strong.
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In this work, epoxy-sized textile-grade polyacrylonitrile (PAN) carbon fiber (TCF) with 450 K filaments (CFTF, ORNL) was reinforced in the polycarbonate (PC) matrix using a compression molding technique. The epoxy sizing effect on the surface, thermal, and mechanical properties of TCF–PC was investigated. X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy results of the TCF–PC composite show the absence of an oxirane group (epoxy-sized TCF), which suggests a covalent bond formation between the oxirane ring and carbonyl groups from TCF and PC. Dynamic mechanical analysis results confirm a strong immobilized interface (b > 1) present between TCF and PC. The tensile, flexure, compression, and interlaminar shear strength of TCF–PC composites were 323 ± 53, 371 ± 31, 397 ± 87, and 36 ± 3 MPa, correspondingly. The fracture analysis through scanning electron microscopy shows good wettability between TCF and the PC matrix which validates the results obtained from surface and thermal characterization.
Abstract Shape‐persistent, conductive ionogels where both mechanical strength and ionic conductivity are enhanced are developed using multiphase materials composed of cellulose nanocrystals and hyperbranched polymeric ionic liquids (PILs) as a mechanically strong supporting network matrix for ionic liquids with an interrupted ion‐conducting pathway. The integration of needlelike nanocrystals and PIL promotes the formation of multiple hydrogen bonding and electrostatic ionic interaction capacitance, resulting in the formation of interconnected networks capable of confining a high amount of ionic liquid (≈95 wt%) without losing its self‐sustained shape. The resulting nanoporous and robust ionogels possess outstanding mechanical strength with a high compressive elastic modulus (≈5.6 MPa), comparable to that of tough, rubbery materials. Surprisingly, these rigid materials preserve the high ionic conductivity of original ionic liquids (≈7.8 mS cm −1 ), which are distributed within and supported by the nanocrystal network‐like rigid frame. On the one hand, such stable materials possess superior ionic conductivities in comparison to traditional solid electrolytes; on the other hand, the high compression resistance and shape‐persistence allow for easy handling in comparison to traditional fluidic electrolytes. The synergistic enhancement in ion transport and solid‐like mechanical properties afforded by these ionogel materials make them intriguing candidates for sustainable electrodeless energy storage and harvesting matrices.
The Hofstadter–Hubbard model captures the physics of strongly correlated electrons in an applied magnetic field, which is relevant to many recent experiments on Moiré materials. Few large-scale, numerically exact simulations exists for this model. In this work, we simulate the Hubbard–Hofstadter model using the determinant quantum Monte Carlo (DQMC) algorithm. We report the field and Hubbard interaction strength dependence of charge compressibility, fermion sign, local moment, magnetic structure factor, and specific heat. The gross structure of magnetic Bloch bands and band gaps determined by the non-interacting Hofstadter spectrum is preserved in the presence of U. Incompressible regions of the phase diagram have improved fermion sign. At half filling and intermediate and larger couplings, a strong orbital magnetic field delocalizes electrons and reduces the effect of Hubbard U on thermodynamic properties of the system.
Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.
Nanoindentation Continuous Stiffness Measurement (CSM) mapping, and Strain Rate Jump Testing (SRJT) proved effective to highlight and visualize the extent of property differences between the samples. From the analysis, Lot A has a significant dead zone at its center, especially when compared with the cross section of Lot B. Quick identification of property issues from one of the two feed stocks, both of which fit the specification. Mechanical performance was connected to the microstructure. Thus, using the structure-property results, it is noted that the processing of Lot A may have been insufficient. Nanoindentation allows for the sampling of quality control variables such as hardness and modulus in a short time span at high resolution, which is cheaper and faster than traditional strength experiments (tension and compression tests).
Conventional Split Hopkinson Pressure Bars (SHPB) or “Kolsky” bars are often used for determining the high-rate compressive yield and failure strength of materials. However, for experiments generating very high strain-rates (>10 3 /s) miniaturization of the setup is often required for minimizing the effects of elastic wave dispersion in order to enable the inference of decreasingly short loading events from the data. Miniature aluminum and steel bars are often sufficient for meeting these requirements. However, for high enough strain-rates, miniaturization of steel or aluminum Kolsky bars may require prohibitively small diameter bars and test specimens that could become inappropriate for inferring representative properties of materials with large grain size relative to the test specimen size. The use of a beryllium Kolsky bar setup is expected to enable high rates to be accessible with larger diameter bars/specimen combinations due to the inherent physical properties of beryllium, which are expected to minimize the effects of elastic wave dispersion. For this reason, a series of beryllium Kolsky bars have been developed, and, in this paper, the dispersion characteristics of these bars are measured and compare the data with those of similarly sized 7075-T6 aluminum and C350 maraging steel. The results, which agree well with the theory, show no appreciable frequency dependence of the elastic wavespeed in the data from the beryllium bars, demonstrating its advantage over aluminum and steel in application to Kolsky bars.
Baseline Graphite Characterization: Purpose of Baseline, Mechanical Baseline Tests, Physical Properties Tests, Methodology, Baseline progress, Density, Ultimate Tensile Strength, PCEA Ultimate Tensile Strength, Split Disc Tests, Compression Tests, Flexural Tests, and Coefficient of Thermal Expansion.
API X60 steel used for reeled pipeline was subjected to different amounts of cyclic plastic deformation (CPD) by multiple tension–compression tests in order to study the deformation behavior and microstructural evolution during each stage of reel-lay installation process. It was found that the yield strength was significantly reduced by about 110 MPa during the first CPD cycle due to the Bauschinger effect. However, the yield strength was little affected by the subsequent CPD cycles, and the yield strength difference between tension and compression tended to reach a steady-state value of 23 MPa after multiple CPD cycles. With the increase in CPD cycles, the proportion of low-angle grain boundaries declined from 50.9 to 35.7%, and the CPD contributed to the formation of immovable dislocation walls and cells due to the dislocation rearrangement, causing the stable yield strength. The CPD process with strain level of 3% had no prominent effect on the tensile strength and elongation, because the change in microstructures was not enough to alter the work hardening completely after multiple CPD cycles.
The micropillar compression test is a novel experiment to study the mechanical properties of materials at small length scales of micro and nano. The results of the micropillar compression experiments show that the strength of the material depends on the pillar diameter, which is commonly termed as size effects. In the current work, first, the experimental observations and theoretical models of size effects during micropillar compression tests are reviewed in the case of crystalline metals. In the next step, the recent computer simulations using molecular dynamics are reviewed as a powerful tool to investigate the micropillar compression experiment and its governing mechanisms of size effects.