Characterisation of heterogeneous microstructure in large forged products using nonlinear ultrasonic method
TL;DR: In this article, a comparative study between the ultrasonic attenuation and the nonlinearity is presented for the characterisation of microstructure in large dimension forgings, and results are provided for two austeni...
Abstract: A comparative study between the ultrasonic attenuation and the nonlinearity is presented for the characterisation of microstructure in large dimension forgings. Results are provided for two austeni...
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TL;DR: In this paper, the microoxide inclusions of AM metal parts were evaluated using a nonlinear ultrasonic technique and the results of the proposed method were consistent with the metallography and tensile testing results.
11 citations
TL;DR: In this paper, the effect of grain size distribution on the measured acoustic nonlinearity of polycrystalline engineering materials is investigated, and the results predict that the existing models that account for only the mean grain size when characterizing material degradations need to be modified more comprehensively to include the role of grain sizes distribution.
Abstract: The effect of grain size distribution on the measured acoustic nonlinearity of polycrystalline engineering materials is investigated. Results are provided for two austenitic stainless steel materials with comparable mean grain sizes and distinct distribution widths assuming equiaxed grains and random crystallographic orientation. The distribution width is shown to influence the nonlinearity parameter considerably. On the material with a wider distribution, a reduced nonlinearity was noted, and comparable trends were also noted for different frequencies investigated. The results predict that the existing models that account for only the mean grain size when characterizing material degradations need to be modified more comprehensively to include the role of grain size distribution.
6 citations
TL;DR: In this article , a deep learning framework was proposed to predict the timing and size of the laboratory earthquakes with great accuracy using the elastic wave attributes (physics-based wave speed and amplitude).
Abstract: Prediction of micro-earthquakes when monitoring geothermal reservoirs, CO2 storage sites, and unconventional reservoirs using active source seismic data although challenging, is essential to ensuring the safety of the operations. Laboratory-scale friction experiments have shown that changes in elastic wave amplitude and speed during active source monitoring carry precursory information about the upcoming failure. The friction experiment is conducted close to the stability boundary producing numerous regular and irregular seismic cycles. A pair of the P-wave ultrasonic transducer is used to probe the laboratory fault throughout the seismic cycles. The data-driven deep learning models predict the timing and size (shear stress drop) of the laboratory earthquakes with great accuracy using the elastic wave attributes (physics-based wave speed and amplitude). In the active source ultrasonic monitoring, the ultrasonic signals need to be truncated to extract the wave features, and the rest of the information is discarded. A few hand-picked features are extracted from this reduced data. So, to overcome the data reduction limitation a novel method extracting the ultrasonic features automatically from the entire signal is demonstrated using a deep learning framework. In this, the recorded ultrasonic signals are passed through a convolutional neural network (CNN) to extract the features automatically. Furthermore, feature importance and an optimum number of feature selections are carried out using the XGBoost method. Finally, Long Short-term Memory (LSTM) model is developed using the time history of these features to predict the failure. Results show that the timing and size of the failure can be predicted with high confidence. The data-driven models require training on large datasets and overlook the physical laws controlling the shear failure. As such, the model may perform well for a particular dataset but fail to provide satisfactory predictions for a different albeit closely related dataset. To incorporate the domain knowledge and address the model transferability challenge, in this study, a physicsinformed deep learning approach is also implemented to forecast failure. The data-driven predictions obtained using the Multi-layer Perceptron (MLP) & LSTM model are used as a baseline. Along with shear stress, the shear failure rate (fault slip rate) is also predicted and used in the physical constraint formulations. The rate-and-state friction law and elastic coupling relation are integrated into the deep learning model architecture to modify the loss function. We hypothesize that a proposed physics-informed deep learning framework can improve model generalizability, prediction of an earthquake, and micro-seismicity informed by the physics laws dictating the shear failure state.
TL;DR: In this article , the combined effect of grain size variation and plastic deformation on the acoustic nonlinearity parameter has been investigated in an austenitic stainless-steel material of grade 304.
Abstract: The combined effect of grain size variation and plastic deformation on the acoustic nonlinearity parameter has been investigated in an austenitic stainless-steel material of grade 304. The nonlinear behavior of this parameter with grain growth has deviated to linear fit with deformation. This is due to the interaction of elastic waves with the strain-induced dislocation substructure in the grains. The normalized mean square strain of the deformed specimens has been estimated through angle dispersive x-ray diffraction studies using a synchrotron source, and this has been correlated with the change in the acoustic non-linearity parameter with deformation. The nonlinearity parameter is found to be very sensitive to the plastic deformation even in the presence of grain size variations. The results infer that the variations in the nonlinearity parameter can be used to have an estimate of the extent of localized deformations often occurring during the fabrication of metallic components.
TL;DR: In this article , the nonlinear effect caused by the heterogeneous structure of carbon fiber reinforced polymer (CFRP) components was investigated and nonlinear frequency mixing of counter-propagating A0-S0 mode Lamb waves was numerically and experimentally investigated in the CFRP composite plate.
Abstract: Delamination is unavoidable during the fabrication or utilisation of carbon fibre reinforced polymer (CFRP) components. Due to the high sensitivity of the A0 mode for detecting delamination, nonlinear frequency mixing of counter-propagating A0-S0 mode Lamb waves was numerically and experimentally investigated in the CFRP composite plate. Considering the nonlinear effect caused by the heterogeneous structure of CFRP composite, the counter-propagation of primary Lamb waves can minimise the nonlinear interaction induced by material nonlinearity compared to the one-way propagation. Numerically, the nonlinear interaction between primary Lamb waves and delamination was investigated. Moreover, delaminations with different sizes, depths, and interface gaps were explored. It was found that the acoustic nonlinear parameter grows with expanding delamination size and diminishes with increasing delamination depth and interface gap. Furthermore, the nonlinear frequency mixing of counter-propagating A0-S0 mode Lamb wave was experimentally performed. The nonlinear frequency mixing effect induced by the delamination can be maximally isolated from the material nonlinearity. Consequently, the technique based on the nonlinear frequency mixing of counter-propagating A0-S0 mode Lamb waves provides a potential and effective method to detect defects in the composite plate with heterogeneous structures.
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TL;DR: In this article, the second-order acousto-elastic coefficient (SOC) was measured in a variety of materials including plastics, metals, composites and adhesives.
430 citations
TL;DR: In this article, the authors developed a robust experimental procedure to track the evolution of fatigue damage in a nickel-base superalloy with the acoustic nonlinearity parameter, β, and demonstrates its effectiveness by making repeatable measurements of β in multiple specimens, subjected to both high and low-cycle fatigue.
Abstract: This research develops a robust experimental procedure to track the evolution of fatigue damage in a nickel-base superalloy with the acoustic nonlinearity parameter, β, and demonstrates its effectiveness by making repeatable measurements of β in multiple specimens, subjected to both high- and low-cycle fatigue. The measurement procedure developed in this research is robust in that it is based on conventional piezoelectric contact transducers, which are readily available off the shelf, and it offers the potential for field applications. In addition, the measurement procedure enables the user to isolate sample nonlinearity from measurement system nonlinearity. The experimental results show that there is a significant increase in β linked to the high plasticity of low-cycle fatigue, and illustrate how these nonlinear ultrasonic measurements quantitatively characterize the damage state of a specimen in the early stages of fatigue. The high-cycle fatigue results are less definitive (the increase in β is not as...
428 citations
Additional excerpts
...NLU has beenwidely used in characterisation of fatigue damage [20], creep damage [21], precipitation kinetics [22], micro cracking [23], cold work [24] and grain boundary precipitates [25]....
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TL;DR: In this paper, a unified approach to solve for the attenuation and phase velocity variations of elastic waves in single phase, polycrystalline media due to scattering is presented. But the approach is not applicable for any material whose singlecrystal anisotropy is not large, regardless of texture, grain elongation, or multiple scattering.
Abstract: We have developed a unified approach to solve for the attenuation and phase velocity variations of elastic waves in single‐phase, polycrystalline media due to scattering. Our approach is a perturbation method applicable for any material whose single‐crystal anisotropy is not large, regardless of texture, grain elongation, or multiple scattering. It accurately accounts for the anisotropy of the individual grains. It is valid for time‐harmonic waves with all ratios of grain size to wavelength. It uses an autocorrelation function to characterize the geometry of the grains, and thereby avoids coherent artifacts that occur if the grains are assumed to have symmetrical shapes and suggests new methods for characterizing distributions of grains that are irregularly shaped. We have carried out numerical calculations for materials that are untextured and equiaxed, and have cubic‐symmetry grains and an inverse exponential spatial autocorrelation function. These calculations agree with the previous calculations which are valid in the Rayleigh, stochastic, and geometric regions, and show the transitions between these regions. The complex transition between the Rayleigh and stochastic regions for longitudinal waves, and the severe limitations of the stochastic region for grains with fairly large anisotropy are of particular interest.
354 citations
"Characterisation of heterogeneous m..." refers background in this paper
...A higher attenuation observed in pure niobium [28] than that predicted by the unified grain scattering theory [40] was explained based on the presence of larger grains in the microstructure....
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TL;DR: In this paper, the grain size distributions and ultrasonic velocities in fine-grained specimens of several metals were determined and the experimental attenuation was in good quantitative as well as qualitative agreement with current theory.
Abstract: Ultrasonic‐attenuation measurements have been made on fine‐grained specimens of several metals. The grain‐size distributions and ultrasonic velocities in these metals were also determined. The experimental attenuation is in good quantitative as well as qualitative agreement with current theory. Nickel and three iron alloys, one 30% nickel reported previously, the second 12% chromium (type 416 stainless steel), and the third 17% chromium and 1% carbon (type 440‐C stainless steel), all gave good results. Brass also gave good results, but copper showed much twinning, which as yet is unaccounted for.
190 citations
"Characterisation of heterogeneous m..." refers background in this paper
...Elastic waves in a polycrystalline material attenuate due to scattering and absorption mechanisms [27]....
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TL;DR: In this paper, the authors used ultrasonic velocity measurements to estimate average grain size in an AISI type 316 stainless steel, and the results indicated that grain size can be predicted with good confidence level using ultrasonic velocities.
Abstract: Ultrasonic velocity measurements have been used to estimate average grain size in an AISI type 316 stainless steel. For precise ultrasonic transit time measurements, the pulse-echo-overlap technique has been used. Master graphs relating ultrasonic velocity with metallographically obtained grain size have been generated. Using these graphs, grain sizes in new specimen have been obtained. The results indicate that grain size can be predicted with good confidence level using ultrasonic velocity measurements. Shear waves are found to be more sensitive for grain size measurement, as compared to longitudinal waves. The grain size estimated by velocity measurements is found to be more accurate when compared to that obtained by attenuation measurements.
144 citations
"Characterisation of heterogeneous m..." refers background in this paper
...Though the attenuation measurements possess higher sensitivity over the velocity measurements [13], it failed in identifying different phases [16,17]....
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...In this context, ultrasonic velocity [12] and attenuation [13] measurements are considered even on the industrial scale....
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