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At least 271 records · Page 15

Mesoscopic-scale complexity in macroscopically-uniform plastic flow of an Al 0.3 CoCrFeNi high-entropy alloy

The concept of a smooth and homogeneous plastic flow of solids is nowadays constantly challenged by various observations of the self-organization of crystal defects on mesoscopic scales pertaining, e.g., to acoustic emission or the evolution of the local strain field. Such investigations would be of particular interest for High-Entropy Alloys (HEAs) characterized by extremely complex microstructures. However, the complexity of their deformation has been only studied in relation to the jerky flow. The present work explores a mesoscale complexity of the macroscopically-smooth plastic flow of an Al 0.3 CoCrFeNi alloy. Here, an inclusive approach involving various experimental methods and complementary nonlinear analyses allowed to reveal transitions between distinct dynamical regimes, which may be assimilated with noises of different colors, in particular the blue noise that is rarely observed in complex systems, and various kinds of reddened behavior. Such findings provide significant new insights into the micro/macro transition in the deformation behavior of HEAs.

36 MATERIALS SCIENCE↗

Trustworthy Machine Learning for Damage Identification in Composites

A challenging opportunity in structural health monitoring of composite materials is using machine learning (ML) methods to classify acoustic emissions (AE) according to the damage mechanism that emitted the signal. Although a wide variety of ML frameworks have been developed, there is a distinct lack of ground truth datasets which has precluded any direct assessment of their accuracy. Here, we present a novel ground truth dataset gathered on simplified unidirectional SiC/SiC composite structures. Herein, AE is collected from minicomposites which are loaded to targeted percentages of the ultimate tensile stress. These minicomposites are then volumetrically imaged with XCT and individual damage events, along with the mechanism, are correlated to AE. We explore the signal features that allow for mechanism discrimination, along with the feasibility of both unsupervised and supervised frameworks for use in the online monitoring of composite structures.

Machine learning, acoustic emission, ceramic matri↗

Mechanically induced thermal runaway severity analysis of Li-ion batteries and continuous energy release monitoring

The large-scale deployment of Li-ion batteries in stationary energy storage and electrical vehicle applications demands a strong focus on safety, particularly on the thermal runaway risk and severity evaluation. A standardized single-side mechanical indentation test protocol was developed to induce an internal short-circuit (ISC) and evaluate cells' thermal runaway severity at different state of charge (SOC). The observed hazard severity (OHS in five categories) and evaluated scores in this work have a comprehensive consideration of each cell's capacity, initial voltage, SOC, temperature and voltage change, allowing a better evaluation of the cells' thermal runaway potential. This method was applied to about 200 Li-ion batteries in order to build an extensive thermal runaway database covering various SOCs, capacities and chemistries. In this study, we monitored the transitions of stored electrochemical energy and applied mechanical energy into both thermal energy and acoustic emissions (AE). The surface temperature and mechanical failures were monitored by infrared imaging and AE to capture critical events within battery cells throughout the mechanical indentation tests. Furthermore, the initial temperature maps can predict two types of follow-up events: thermal runaway or gradual heat release via conduction. Analyzing each cell's severity, AEs, and leveraging the evolving database offer insights into predicting occurrences of thermal runaway. The test method, thermal runaway severity evaluation and prediction, and the corresponding database provide battery designers, manufacturers, and end-users a clear overview of Li-ion batteries' thermal runaway potential under mechanical abuse, advancing the safety design of Li-ion batteries.

Acoustic emission↗

Transformational faulting in Mn 2 GeO 4 from olivine to wadsleyite structure: Implications for physical mechanism of deep-focus earthquakes

High-pressure and temperature deformation experiments interfaced with acoustic emission (AE) monitoring have been conducted to study transformational faulting in Mn 2 GeO 4 olivine, which transforms to the β phase, isostructural to wadsleyite. Metastable Mn 2 GeO 4 olivine exhibits a marked embrittlement behavior at temperatures between 800 and 1100 K, emitting numerous AEs. At each temperature, brittle deformation is characterized by a two-stage process: (1) a “preparation” stage with numerous diffusedly located low-magnitude AEs and large b values (>2), and (2) a failure stage where larger-magnitude AEs form a planar distribution with b values about 1. Microstructure analysis reveals extensive kink band development in olivine grains in the recovered samples. Kink band boundaries (KBBs), with a typical thickness of ∼100 nm, are filled with a nanometric β-Mn 2 GeO 4 “gouge”. A dense array of secondary shear localizations is often present within the kink bands, suggesting significant shear deformation therein. The combined observations suggest that faulting in metastable Mn 2 GeO 4 olivine is a self-similar process, from grain-scale to the sample-scale. Both observed embrittlement behavior and the microstructure of metastable Mn 2 GeO 4 olivine are essentially identical to those in Mg 2 GeO 4 olivine we have reported previously, indicating that the physical mechanism of faulting in metastable olivine is insensitive to the specific crystallographic structure of the high-pressure phase. The low b values (about 1) observed in the faulting process in our experiments are similar to those of deep focus earthquakes in cold subduction zones. Our observed mechanism explains deep focus seismicity in cold metastable mantle wedges, provided that the self-similarity assumption holds to geological scales.

58 GEOSCIENCES↗

A prediction model of failure threshold for shear deformation in a Zr-based bulk metallic glass

The failure of bulk metallic glasses (BMGs) during plastic deformation at room temperature is abrupt and instantaneous, while the analysis of precursor information based on avalanche events helps predict catastrophic failure. An acoustic emission (AE) signal can provide accurate precursor information for material failure, due to its sensitive and high fast calculation ability. In the current study, AE monitoring tests are carried out during uniaxial compression tests of BMGs at different strain rates. The AE experimental failure threshold, E max , is proposed on the basis of AE cumulative energy, which reflects the intensity of damage evolution at different loading conditions. Compared with the critical shear band velocity (CSBV) associated with stick-slip dynamics of serrated flow, E max is a more sensitive failure parameter since it is connected with the local microscopic changes that occur during the material response process. Here, the E max is obtained prior to reaching the CSBV since the calculation of these two avalanches analysis focuses on the different stages of shear band growth. In particular, AE events are related to the “dry” friction process in the first stage, however, the CSBV is responsible for the “viscous” glide in the second stage. Therefore, Emax is not affected by the complex interactions between the shear bands during the stick-slip process. The maximum avalanche of serrated flow, S max , is proposed as the experimental failure threshold, which depends on the applied strain rate as S max ~ $\dot{ε}$ –λ . According to the relationship of E max and S max , the theoretical failure threshold, E max , follows a criterion E max = 2545$\dot{ε}$ –λ - 4468, where λ is equivalent to 0.15 for this work. Finally, combining the different calculations and AE measurements, this model gives new insights to predict the deformation failure behavior of Zr-based BMGs.

36 MATERIALS SCIENCE↗

Geophysical Signatures of Crack Network Coalescence in Rocks at Multiple Length Scales

The main goal of the research project was to identify the geophysical signatures of fracture growth in natural rocks by utilizing novel geophysical techniques. The research objectives were to (a) investigate the potential for geophysical methods to determine when cracks initiate, the types and locations of propagated cracks, and the coalescence of networks of cracks in natural rocks at multiple scales, (b) determine how damage at the microscale evolved into damage at the macroscale and then link the microscopic and macroscopic observations, (c) quantify crack coalescence in rocks under realistic stress conditions using coupled mechanical-geophysical-optical visualization, and (d) identify the precursors in geophysical signals to crack coalescence. The following research thrusts were explored to achieve the research objectives: (1) uniaxial compression testing of rock specimens with and without a set of pre‐existing flaws and (2) triaxial compression testing of natural rock specimens. These thrusts allowed for exploring fracturing in rocks under realistic in situ environments and at multiple scales. This project provided educational opportunities for nine graduate and undergraduate students and resulted in 27 peer-reviewed publications. This first research thrust focused on investigating the micromechanics of fractures in rocks through uniaxial compression testing combined with advanced geophysical and imaging techniques, specifically acoustic emission (AE) monitoring, ultrasonic imaging, and 2-dimensional Digital Image Correlation (2D-DIC). By examining damage processes under time-independent and time-dependent loading conditions, insights into damage localization, crack initiation, and fracturing mechanisms were gained. It was observed that the AE signals and the strain-based measurements directly reflect the state of damage in the rock specimen and could be used to identify the cracking levels, such as the crack initiation (CI) and crack damage (CD), and the mode of deformation. A novel calibration apparatus was developed to enhance the accuracy of AE sensors, allowing for the estimation of key parameters such as magnitude, source dimension, stress drop, and radiated seismic energy associated with the fractures. The findings highlighted significant variations in the temporal evolution of AE source parameters during the primary, secondary, and tertiary stages of creep, identifying tensile cracking as the primary deformation mode. The second research thrust focused on enhancing the understanding of fracturing processes in natural rocks through triaxial compression testing, real-time AE monitoring, and ultrasonic monitoring. We investigated the impact of various factors such as fracture propagation regimes, injection parameters, rock types, and pre-existing conditions on the hydraulic fracture (HF) behavior using scaled true-triaxially loaded specimens of Barre granite and Lyons sandstone. Custom sensor housing facilitated concurrent active and passive monitoring to analyze hydro-mechanical responses and microseismicity associated with different HF scenarios. A coupled investigation of passive microseismicity and active signal attributes permitted a detailed comprehension of the various HF processes (aseismic deformation, fracture initiation and propagation, fluid permeation, and leak-off) and their dependence on the specific rock type. The findings of this research demonstrated the effectiveness of AE monitoring techniques in providing valuable insights into the impact of various factors on the behavior and dynamics of HF processes. The advancements in monitoring techniques, offering a more thorough and precise approach, represent a significant step towards optimizing HF practices and ensuring sustainable resource extraction.

58 GEOSCIENCES↗

Effect of Thin-Film Adhesives on Mode I Interlaminar Fracture Toughness in Carbon Fiber Composites with Shape Memory Alloy Inserts

A single sheet of NiTi shape memory alloy (SMA) was introduced within a unidirectional HexPly® 8552/IM7 (Hexcel®) polymer matrix composite (PMC) panel in conjunction with multiple thin-film adhesives to promote the interfacial bond strength between the SMA and PMC. A double cantilever beam (DCB) test was performed in accordance with the ASTM D5528 method for evaluation of Mode I interlaminar fracture toughness of unidirectional fiber-reinforced PMCs. The modal acoustic emissions (MAEs) were monitored during testing with two acoustic sensors attached to the specimens. The composite panels were subjected to a C-scan before testing and examined using optical and scanning electron microscopy (SEM) techniques after part failure. The data were used in conjunction with modified beam theory (MBT), the compliance calibration (CC) method, and the modified compliance calibration (MCC) method. The Mode I interlaminar toughness (G(sub IC)) values for control specimens were higher than previously reported and are attributed to extensive fiber bridging during testing. The presence of adhesives with SMA inserts stabilized crack propagation during DCB testing. The results reveal a new phenomenon of SMA bridging, whereby crack propagation would switch from one side of the SMA insert to the other, thus increasing the load and G(sub IC) values of specimens containing SMA.

shape memory alloys↗

Effect of Thin-Film Adhesives on Mode II Interlaminar Fracture Toughness in Carbon Fiber Composites with Shape Memory Alloy Inserts

A single sheet of nickel-titanium (NiTi) shape memory alloy (SMA) was introduced within an IM7/8552 polymer matrix composite (PMC) panel in conjunction with multiple thin film adhesives to promote the interfacial bond strength between the SMA and PMC. End notched flexure (ENF) testing was performed in accordance to ASTM D7905 method for evaluation of mode II interlaminar fracture toughness (GIIC) of unidirectional fiber-reinforced polymer matrix composites. Acoustic emissions (AE) were monitored during testing with two acoustic sensors attached to the specimens. The composite panels were subjected to C-scan before testing, and examined using optical and scanning electron microscopy techniques after part failure. GIIC values for the control composite samples were found to be higher than those of samples with embedded SMA sheets. The presence of adhesives bonded to SMA sheets further diminished the GIIC values. AE values revealed poor bonding of the panels, with little to no signals during testing.

End Notched Flexure↗

The Effect of Thin Film Adhesives on Mode II Interlaminar Fracture Toughness in Carbon Fiber Composites with Shape Memory Alloy Inserts

A single sheet of nickel-titanium (NiTi) shape memory alloy (SMA) was introduced within an IM7/8552 polymer matrix composite (PMC) panel in conjunction with multiple thin film adhesives to promote the interfacial bond strength between the SMA and PMC. End notched flexure (ENF) testing was performed in accordance to ASTM D7905 method for evaluation of mode II interlaminar fracture toughness (GIIC) of unidirectional fiber-reinforced polymer matrix composites. Acoustic emissions (AE) were monitored during testing with two acoustic sensors attached to the specimens. The composite panels examined using scanning electron microscopy techniques after part failure. GIIC values for the control composite samples were found to be higher than those of samples with embedded SMA sheets. The presence of adhesives bonded to SMA sheets further diminished the GIIC values. AE values revealed poor bonding of the panels, with little to no signals during testing.

End Notched Flexure↗

Non-destructive evaluation and machine learning methods for inspection of spent nuclear fuel canisters: A state-of-the-art review

Nuclear energy is among the cleanest and most efficient energy sources currently available. The operation of nuclear power plants (NPPs) produces large amounts of high-level radioactive waste known as spent nuclear fuel (SNF). Currently, large amounts of SNF is stored in dry cask storage systems (DCSSs) for extended interim storage until a permanent disposal solution becomes available. During the extended interim storage, the DCSS, particularly the SNF canisters, may degrade and abnormal conditions may occur. Therefore, non-destructive evaluation (NDE) and machine learning (ML) approaches are necessary for inspection of SNF canisters. This paper presents a state-of-the-art review of literature by summarizing recent progress made on the applications of NDE and ML for inspection of SNF canisters. Sixteen NDE methods are examined and compared: visual inspection, ultrasonic guided waves (UGWs), laser-based approaches, acoustic emission (AE), eddy current testing (ECT), non-invasive acoustic sensing, dynamic modal testing, cosmic ray muons tomography, neutron imaging, gamma rays detection, fiber optical sensors, through-wall communications, X-ray computed tomography (CT), vibrothermography, monoenergetic photon sources, and surface acoustic wave (SAW) sensors. The technology readiness level (TRL) for each method is assessed and compared. Recent publications on ML-enhanced visual inspection, AE, non-invasive acoustic sensing, dynamic modal testing, and neutron imaging for SNF canisters are summarized and future research needs are identified. In conclusion, this review article provides a convenient reference on the state-of-the-art applications of NDE and ML methods for inspection of SNF canisters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗

Acoustic noise generation by the DOE/NASA MOD-1 wind turbine

The results of a series of measurements taken over the past year of the acoustic emissions from the DOE/NASA MOD-1 Wind Turbine show the maximum acoustic energy is concentrated in the low frequency range, often below 100 Hz. The temporal as well as the frequency characteristics of the turbine sounds have been shown to be important since the MOD-1 is capable of radiating both coherent and incoherent noise. The coherent sounds are usually impulsive and are manifested in an averaged frequency domain plot as large numbers of discrete energy bands extending from the blade passage frequency to beyond 50 Hz on occasion. It is these impulsive sounds which are identified as the principal source of the annoyance to a dozen families living within 3 km of the turbine. The source of the coherent noise appears to be the rapid, unsteady blade loads encountered as the blade passes through the wake of the tower structure. Annoying levels are occasionally reached at nearby homes due to the interaction of the low frequency, high energy peaks in the acoustic impulses and the structural modes of the homes as well as by direct radiation outdoors. The peak levels of these impulses can be enhanced or subdued through complete propagation.

Kelley, N. D.↗

Validation and Simulation of Ares I Scale Model Acoustic Test - 2 - Simulations at 5 Foot Elevation for Evaluation of Launch Mount Effects

The Ares I Scale Model Acoustics Test (ASMAT) is a series of live-fire tests of scaled rocket motors meant to simulate the conditions of the Ares I launch configuration. These tests have provided a well documented set of high fidelity measurements useful for validation including data taken over a range of test conditions and containing phenomena like Ignition Over-Pressure and water suppression of acoustics. Expanding from initial simulations of the ASMAT setup in a held down configuration, simulations have been performed using the Loci/CHEM computational fluid dynamics software for ASMAT tests of the vehicle at 5 ft. elevation (100 ft. real vehicle elevation) with worst case drift in the direction of the launch tower. These tests have been performed without water suppression and have compared the acoustic emissions for launch structures with and without launch mounts. In addition, simulation results have also been compared to acoustic and imagery data collected from similar live-fire tests to assess the accuracy of the simulations. Simulations have shown a marked change in the pattern of emissions after removal of the launch mount with a reduction in the overall acoustic environment experienced by the vehicle and the formation of highly directed acoustic waves moving across the platform deck. Comparisons of simulation results to live-fire test data showed good amplitude and temporal correlation and imagery comparisons over the visible and infrared wavelengths showed qualitative capture of all plume and pressure wave evolution features.

Strutzenberg, Louise L.↗

Acoustic target detection and classification using neural networks

A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

Robertson, James A.↗

C-Coupon Studies of SiC/SiC Composites: Microstructual Characterization - Part 2

A 'C-coupon' was used to measure the interlaminar tensile behavior of five different composite materials: Sylramic/MI (melt infiltration SiC matrix), ZMl/MI, Sylramic/S300, and ZMI and Hi-Nicalon with an S200 matrix at room temperature, 816 C, and 1204 C. Fracture behavior was characterized by field emission scanning electron microscopy, and observed cracking compared with events measured by modal acoustic emission. Both radial and matrix hoop cracking along the inner radius arising from hoop stresses were observed for all material types. In those composites in which the radial cracks were sufficiently wide to view the composite interior, fracture was observed to occur at the fiber BN or BN matrix interface.

Hurwitz, Frances I.↗

A study of the use of vibration and stress wave sensing for the detection of bearing failure

Results from an experimental study of vibrations and stress waves emitted from ball bearings are presented. Fatique tests were run with both high quality bearings and man faulted bearings, all of one size. Tests were instrumented with different sensors to detect the noises from 10 Hz to 1 MHz. Frequency spectrum plots are presented. The modulation characteristics of the ultrasonic noises were analyzed, and acoustic emission type measurements were conducted. Results are presented which show that there are usable acoustic signal levels even beyond 500 KHz. These signal levels are modulated by a low frequency carrier which is a function of the stress loading and acoustic transmissibility. The results were correlated to fault size in the bearings. The correlation shows that the sensor used for signals from 100 KHz to 1 MHz gave the best sensitivity and detected the generation of very small spalls or pits.

Ensor, L. C.↗

The source of solar high-frequency acoustic modes - Theoretical expectations

The source exciting the solar p-modes is likely to be acoustic noise generated in the top part of the sun's convection zone. If so, then simple arguments suggest that most of the emitted energy may come from rare localized events that are well separated from one another in space and time. This note describes the acoustic emission that would be expected from such events, based on a ray-theory analysis. Most of the acoustic energy is found to emerge very close to the source, so that observations to identify emission events will require high spatial resolution.

Brown, Timothy M.↗

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

36 MATERIALS SCIENCE↗