Noise and interference in sensor measurements degrade the quality of data and have a negative impact on the
performance of structural damage diagnosis systems. In this paper, a novel adaptive measurement screening
approach is presented to automatically select the most informative measurements and use them intelligently for
structural damage estimation. The method is implemented efficiently in a sequential Monte Carlo (SMC) setting
using particle filtering. The noise suppression and improved damage estimation capability of the proposed method
is demonstrated by an application to the problem of estimating progressive fatigue damage in an aluminum
compact-tension (CT) sample using noisy PZT sensor measurements.
The quantification of variability in the mechanical behavior of metallic materials is important in the design and
reliability assessment of mechanical components. A combination of experimental and computational approaches
is often required to alleviate the experimental burden and lack of data in constructing a probabilistic formalism
for material design. The present work aims at integrating material characterization and computational modeling
for the evaluation of variability in the elastodynamic response of random polycrystals. First, a procedure is
presented for simulation of random 2D polycrystalline microstructures from limited experimental data. Second,
the capability of the numerical model in capturing the variation of the scattered waves due to the random
heterogeneities is investigated by introducing a suitable quantity of interest characterizing the intensity of the
fluctuations of the stochastic waveforms. Two important types of heterogeneities are considered. The first is the
inherent heterogeneity due to the mismatch in the grain orientations. The second is the heterogeneity due to fine
scale defects in the form of random intergranular micro-cavities. The numerical model presented in this paper
can be useful for the interpretation of experimental ultrasonic measurements for random heterogeneous material.
The result is also applicable to the validation of multiscale probabilistic models for material prognosis.
The accurate estimation of fatigue life of metallic structural components in service environments is still a challenge for
the aircraft designer or fleet manager. Majority of the current available fatigue life prediction models has deficiency to
accurately predict damage under random or flight profile service loads. The inherent accuracy is due to the stochastic
nature of crack propagation in metallic structure. In addition, currently no generic prediction model available accounting
the load interaction effects due to variable loading. In the present paper we discus the use of a Generic Bayesian
framework based Gaussian process approach to probabilistically predict the fatigue damage under complex random and
flight profile loading.
S. Luo, S. Greenfield, D. Paisley, R. Johnson, T. Shimada, D. Byler, E. Loomis, S. DiGiacomo, B. Patterson, K. McClellan, R. Dickerson, P. Peralta, A. Koskelo, D. Tonks
We present two laser driven shock wave loading techniques utilizing long pulse lasers, laser-launched flyer plate
and confined laser ablation, and their applications to shock physics. The full width at half maximum of the drive
laser pulse ranges from 100 ns to 10 μs, and its energy, from 10 J to 1000 J. The drive pulse is smoothed with
a holographic optical element to achieve spatial homogeneity in loading. We characterize the flyer plate during
flight and dynamically loaded target with temporally and spatially resolved diagnostics. The long duration
and high energy of the drive pulse allow for shockless acceleration of thick flyer plates with 8 mm diameter
and 0.1-2 mm thickness. With transient imaging displacement interferometry and line-imaging velocimetry, we
demonstrate that the planarity (bow and tilt) of the loading is within 2-7 mrad (with an average of 4±1 mrad),
similar to that in conventional techniques including gas gun loading. Plasma heating of target is negligible in
particular when a plasma shield is adopted. For flyer plate loading, supported shock waves can be achieved.
Temporal shaping of the drive pulse in confined laser ablation enables flexible loading, e.g., quasi-isentropic,
Taylor-wave, and off-Hugoniot loading. These dynamic loading techniques using long pulse lasers (0.1-10 μs)
along with short pulse lasers (1-10 ns) can be an accurate, versatile and efficient complement to conventional
shock wave loading for investigating such dynamic responses of materials as Hugoniot elastic limit, plasticity,
spall, shock roughness, equation of state, phase transition, and metallurgical characteristics of shock-recovered
samples, in a wide range of strain rates and pressures at meso- and macroscopic scales.
We investigate the use of low frequency (10-70 MHz) laser ultrasound for the detection of fatigue damage.
While high frequency ultrasonics have been utilized in earlier work, unlike contacting transducers, laser-based
techniques allow for simultaneous interrogation of the longitudinal and shear moduli of the fatigued material. The
differential attenuation changes with the degree of damage, indicating the presence of plasticity. In this paper, we
describe a structural damage identification approach based on ultrasonic sensing and time-frequency techniques.
A parsimonious representation is first constructed for the ultrasonic signals using the modified matching pursuit
decomposition (MMPD) method. This decomposition is then employed to compute projections onto the various
damage classes, and classification is performed based on the magnitude of these projections. Results are presented
for the detection of fatigue damage in Al-6061 and Al-2024 plates tested under 3-point bending.
KEYWORDS: 3D modeling, 3D microstructuring, Aluminum, Finite element methods, Crystals, Particles, Crystallography, Polishing, Material characterization, Statistical modeling
Prediction of scatter on the mechanical behavior of metallic materials due to microstructural heterogeneity is important,
particularly for damaged metallic structures, where degradation mechanisms such as fatigue can be very sensitive to
microstructure variability, which is also a contributing factor to the scatter observed in the fatigue response of metallic
materials. Two-dimensional (2D) and Three-dimensional (3D) representations of microstructures of 2xxx Al alloys are
created via a combination of dual-scale serial sectioning techniques, with a smaller scale for particles and a larger scale
for grains, Electron Backscattering Diffraction (EBSD) and available meshing and volume reconstruction software. In
addition, "artificial" representations of the grains are also built from measurements of the crystallography and the
geometry of the grains in representative cross sections of the samples. These measurements are then used to define a
Representative Volume Element (RVE) with mechanical properties that are comparable to those in larger length scales,
via simulations performed using finite element models of the RVE. In this work, the characteristics of the RVE are
varied by introducing changes on either geometry, material properties or both and by "seeding" defects that represent
damage (microcraks) or damage precursors (precipitates). Results indicate that models obtained predict the variability on
stress fields expected at the local level, due to crystallographic and geometric variability of the microstructure.
This paper formulates a stochastic model of fatigue crack growth in ductile alloys under variable loading of the center
wing type. This center wing loading has three different load ratios to depict the most demanding operating conditions.
The cumulative distribution function of the crack length estimate is generated by numerically solving a stochastic
differential equation describing the physics of the crack growth. The model parameters are obtained by analyzing each
load span, and the variable model parameter is used in the corresponding load period. Simulations are used to show that
the analytical crack exceedance probability follows the experimental data fairly well.
Fatigue crack growth during the service life of aging aircraft is a critical issue and monitoring of such cracks in structural
hotspots is the goal of this research. This paper presents a procedure for classification and detection of cracks generated
in bolted joints which are used at numerous locations in aircraft structures. Single lap bolted joints were equipped with
surface mounted piezoelectric (pzt) sensors and actuators and were subjected to cyclic loading. Crack length
measurements and sensor data were collected at different number of cycles and with different torque levels. A
classification algorithm based on Support Vector Machines (SVMs) was used to compare signals from a healthy and
damaged joint to classify fatigue damage at the bolts. The algorithm was also used to classify the amount of torque in the
bolt of interest and determine if the level of torque affected the quantification and localization of the crack emanating
from the bolt hole. The results show that it is easier to detect the completely loose bolt but certain changes in torque,
combined with damage, can produce some non-unique classifier solutions.
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