Ultrasonic guided waves have the potential to inspect integrated circuit (IC) packages using wave based techniques due to excellent sub surface penetration through metallic as well as dielectric material. Guided waves in a heterogeneous composite assembly such as an IC package have modes with complex dispersion characteristics due to multiple layers of material with intricate geometry. No analytical solution exists for predicting dispersion in highly anisotropic composites. Numerical methods, such as the finite element method, have been used to model dispersion in composites, however these methods are computationally intensive and not feasible for predicting dispersion in IC packages. In this paper, the time-frequency characteristics of guided waves propagating through a complex IC are studied using the synchrosqueezing transform (SST). This is a transform that has been shown to be robust to bounded signal perturbations, to provide highly localized time and frequency information for highly nonlinear modes, and to reconstruct the signal corresponding to each mode. Reference ultrasonic guided wave signals are collected for the IC package in its healthy and damaged states using piezoelectric transducers to characterize the dispersion modes in the excitation region. Initial results demonstrate that the dispersive mode information from the extracted SST ridges provide an effective damage indicator for IC packaging.
This paper presents the development of a delamination detection framework for integrated circuit packages aiming at quantitative detection of sealant delamination between integrated heat sink and substrate, which is one of the potential failure mechanisms in integrated circuit packages. This method is expected to overcome the destructive nature of most existing techniques and maintain a relatively low cost of development. Ultrasonic guided waves are used as the interrogation method due to their sensitivity to small-size damage and capability of through-thickness penetration. The complexity of the received ultrasonic signals, caused by the geometric heterogeneity, is resolved and interpreted using a time-frequency signal processing technique. The extracted ultrasonic information, including time-of-arrival and amplitude of wave modes received from different sensing paths under multiple excitation frequencies, is used to construct the feature space for training. An unsupervised learning method, multivariate Gaussian model, is implemented as an information fusion and delamination detection tool. The multivariate Gaussian model efficiently investigates the distribution of feature space including correlations between features and flag the outliers without labeled examples. Results from the developed model are compared with two existing evaluation methods, including pullout test and a metric indicating the extent of delamination, which indicates that the developed method possesses a similar level of accuracy.
Advanced composite structures, such as foam core carbon fiber reinforced polymer composites, are increasingly being
used in applications which require high strength, high in-plane and flexural stiffness, and low weight. However, the
presence of in situ damage due to manufacturing defects and/or service conditions can complicate the failure
mechanisms and compromise their strength and reliability. In this paper, the capability of detecting damages such as
delaminations and foam-core separations in X-COR composite structures using non-destructive evaluation (NDE) and
structural health monitoring (SHM) techniques is investigated. Two NDE techniques, flash thermography and low
frequency ultrasonics, were used to detect and quantify the damage size and locations. Macro fiber composites (MFCs)
were used as actuators and sensors to study the interaction of Lamb waves with delaminations and foam-core
separations. The results indicate that both flash thermography and low frequency ultrasonics were capable of detecting
damage in X-COR sandwich structures, although low frequency ultrasonic methods were capable of detecting through
thickness damages more accurately than flash thermography. It was also observed that the presence of foam-core
separations significantly changes the wave behavior when compared to delamination, which complicates the use of wave
based SHM techniques. Further, a wave propagation model was developed to model the wave interaction with damages
at different locations on the X-COR sandwich plate.
The piezoresistivity of carbon nanotube (CNT) reinforced nanocomposites is modeled using a multiscale damage modeling technique. Two phenomena of piezoresistivity are studied, the inherent piezoresistivity of the CNTs and the electrical tunneling effect. The damage model is developed under the framework of continuum damage mechanics (CDM) with a physical damage evolution equation inspired by Molecular Dynamics (MD) simulations. This damage model is applied to a nanocomposite unit cells with randomly dispersed CNTs. Orders of magnitude change in piezoresistivity is observed as the nanocomposite changes from non-damaged state to damaged state. This study provides insights into the prevailing mechanisms associated with piezoresistivity in the damaged and undamaged state of the CNT reinforced nanocomposites at the sub micro scale.
Self-healing materials have the potential to repair induced damage and extend the service life of aerospace or civil components as well as prevent catastrophic failure. A novel technique to provide self-healing capabilities at the nanoscale in carbon nanotube/epoxy nanocomposites is presented in this paper. Carbon nanotubes (CNTs) functionalized with the healing agent (dicyclopentadiene) were used to fabricate self-healing CNT/epoxy nanocomposite films. The structure of CNTs was considered suitable for this application since they are nanosized, hollow, and provide a more consistent size distribution than polymeric nanocapsules. Specimens with different weight fractions of the functionalized CNTs were fabricated to explore the effect of weight fraction of functionalized CNTs on the extent of healing. Optical micrographs with different fluorescent filters showed partial or complete healing of damage approximately two to three weeks after damage was induced. Results indicate that by using CNTs to encapsulate a healing agent, crack growth in self-healing CNT/epoxy nanocomposites can be retarded, leading to safer materials that can autonomously repair itself.
Polymer matrix composites (PMCs) are ubiquitous in engineering applications due to their superior mechanical properties at low weight. However, they are susceptible to damage due to their low interlaminar mechanical properties and poor heat and charge transport in the transverse direction to the laminate. Moreover, methods to inspect and ensure the reliability of composites are expensive and labor intensive. Recently, mechanophore-based smart polymer has attracted significant attention, especially for self-sensing of matrix damage in PMCs. A cyclobutane-based self-sensing approach using 1,1,1-tris (cinnamoyloxymethyl) ethane (TCE) and poly (vinyl cinnamate) (PVCi) has been studied in this paper. The self-sensing function was investigated at both the polymer level and composite laminate level. Fluorescence emissions were observed on PMC specimens subjected to low cycle fatigue load, indicating the presence of matrix cracks. Results are presented for graphite fiber reinforced composites.
Physics-based wave propagation computational models play a key role in structural health monitoring (SHM) and the
development of improved damage quantification methodologies. Guided waves (GWs), such as Lamb waves, provide the
capability to monitor large plate-like aerospace structures with limited actuators and sensors and are sensitive to small
scale damage; however due to the complex nature of GWs, accurate and efficient computation tools are necessary to
investigate the mechanisms responsible for dispersion, coupling, and interaction with damage. In this paper, the local
interaction simulation approach (LISA) coupled with the sharp interface model (SIM) solution methodology is used to
solve the fully coupled electro-magneto-mechanical elastodynamic equations for the piezoelectric and piezomagnetic
actuation and sensing of GWs in fiber reinforced composite material systems. The final framework provides the full
three-dimensional displacement as well as electrical and magnetic potential fields for arbitrary plate and transducer
geometries and excitation waveform and frequency. The model is validated experimentally and proven computationally
efficient for a laminated composite plate. Studies are performed with surface bonded piezoelectric and embedded
piezomagnetic sensors to gain insight into the physics of experimental techniques used for SHM. The symmetric
collocation of piezoelectric actuators is modeled to demonstrate mode suppression in laminated composites for the
purpose of damage detection. The effect of delamination and damage (i.e., matrix cracking) on the GW propagation is
demonstrated and quantified. The developed model provides a valuable tool for the improvement of SHM techniques due
to its proven accuracy and computational efficiency.
We describe a stochastic ltering approach for tracking progressive fatigue damage in structures, wherein physically based damage evolution information is combined with active sensing guided wave measurements. The input waveform used to excite dispersive modes within the structure is adaptively con gured at each time step in order to maximize the damage estimation performance. The damage evolution model is based on Paris Law, and hidden Markov modeling of time-frequency features obtained from received signals is used to de ne the measurement model. Damage state estimation is performed using a particle lter. Results are presented for fatigue crack estimation in an aluminum specimen.
A reliable prognostics framework is essential to prevent catastrophic failure of bridges due to scour. In the U.S., scour accounts for almost 60% of bridge failures. Currently available techniques in the literature for predicting scour are mostly based on empirical equations and deterministic regression models, like Neural Networks and Support Vector Machines, and do not predict the evolution of scour over time. In this paper, we will discuss a Gaussian process model, which includes Bayesian uncertainty for prediction of time-dependent scour evolution. We will validate the model on the experimental data conducted in four different flumes in different conditions. The robustness of the algorithm will also be demonstrated under different scenarios, like lack of training data and equilibrium scour conditions. The results indicate that the algorithm is able to predict the scour evolution with an error of less than 20% for most of the time, and 5% or less given enough training data.
Carbon fiber reinforced composites are used in a wide range of applications in aerospace, mechanical, and civil structures. Due to the nature of material, most damage in composites, such as delaminations, are always barely visible to the naked eye, which makes it difficult to detect and repair. The investigation of biological systems has inspired the development and characterization of self-healing composites. This paper presents the development of a new type of self-healing material in order to impede damage progression and conduct in-situ damage repair in composite structures. Carbon nanotubes, which are highly conductive materials, are mixed with shape memory polymer to develop self-healing capability. The developed polymeric material is applied to carbon fiber reinforced composites to automatically heal the delamination between different layers. The carbon fiber reinforced composite laminates are manufactured using high pressure molding techniques. Tensile loading is applied to double cantilever beam specimens using an MTS hydraulic test frame. A direct current power source is used to generate heat within the damaged area. The application of thermal energy leads to re-crosslinking in shape memory polymers. Experimental results showed that the developed composite materials are capable of healing the matrix cracks and delaminations in the bonded areas of the test specimens. The developed self-healing material has the potential to be used as a novel structural material in mechanical, civil, aerospace applications.
KEYWORDS: Sensors, Chemical species, Aluminum, Temperature metrology, Data modeling, Transducers, Associative arrays, Ferroelectric materials, Signal processing, Sensor fusion
This paper examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage
in a complex metallic structural component in the presence of temperature variations. The goal of this research is to
improve damage localization results for a structural component interrogated at an unknown temperature by developing a
probabilistic and reference-free framework for estimating Lamb wave velocities. The proposed approach for
temperature-independent damage localization involves a model that can describe the change in Lamb wave velocities
with temperature, the use of advanced time-frequency based signal processing for damage feature extraction, estimation
of the actual Lamb wave velocities from transducer signals, and a Bayesian damage localization framework with data
association and sensor fusion. The technique does not require any additional transducers on a component and allows the
estimation of the velocities for the actual Lamb waves present in a component. Experiments to validate the proposed
method were conducted using an aluminum lug joint interrogated with piezoelectric transducers for a range of
temperatures and fatigue crack lengths. Experimental results show the advantages of using a velocity estimation
algorithm to improve damage localization for a component interrogated at both known and unknown temperatures.
Physics-based computational models play a key role in the study of wave propagation for structural health monitoring
(SHM) and the development of improved damage detection methodologies. Due to the complex nature of guided waves
(GWs), accurate and efficient computation tools are necessary to investigate the mechanisms responsible for dispersion,
coupling, and interaction with damage. In this paper, a fully coupled electromechanical elastodynamic model for wave
propagation in a heterogeneous, anisotropic material system is developed. The final framework provides the full three
dimensional displacement and electrical potential fields for arbitrary plate and transducer geometries and excitation
waveform and frequency. The model is validated theoretically and proven computationally efficient. Studies are
performed with surface bonded piezoelectric sensors to gain insight into the physics of experimental techniques used for
SHM. Collocated actuation of the fundamental Lamb wave modes is modeled over a range of frequencies to demonstrate
mode tuning capabilities. The effect of various actuation types commonly used in numerical wave propagation models
on Lamb wave speed are studied and compared. Since many studies, including the ones investigated in this paper, are
difficult to perform experimentally, the developed model provides a valuable tool for the improvement of SHM
techniques.
This paper presents a methodology for determining the existence of delaminations in complex composite structures. The
changes in damage features due to changing temperature are investigated. A Lamb wave based active damage detection
technique is used. The Matching Pursuit Decomposition (MPD), a time frequency based signal processing technique, is
used for feature extraction. The signals from two different test structures, a healthy specimen and a specimen with
seeded delamination, are compared to incorporate the effect of manufacturing variability. Tests are conducted under
varying ambient temperature. The results obtained validate the effectiveness of this approach in detecting delamination.
The development of structural health monitoring techniques leads to the integration of sensing capability within
engineering structures. This study investigates the application of multi walled carbon nanotubes in polymer matrix
composites for autonomous damage detection through changes in electrical resistance. The autonomous sensing
capabilities of fiber reinforced nanocomposites are studied under multiple loading conditions including tension loads.
Single-lap joints with different joint lengths are tested. Acoustic emission sensing is used to validate the matrix
crack propagation. A digital image correlation system is used to measure the shear strain field of the joint area. The
joints with 1.5 inch length have better autonomous sensing capabilities than those with 0.5 inch length. The
autonomous sensing capabilities of nanocomposites are found to be sensitive to crack propagation and can
revolutionize the research on composite structural health management in the near future.
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.
Composite materials are widely used in many applications for their high strength, low weight, and tailorability for
specific applications. However, the development of robust and reliable methodologies to detect micro level damage in
composite structures has been challenging. For composite materials, attenuation of ultrasonic waves propagating through
the media can be used to determine damage within the material. Currently available numerical solutions for attenuation
induce arbitrary damage, such as fiber-matrix debonding or inclusions, to show variations between healthy and damaged
states. This paper addresses this issue by integrating a micromechanics analysis to simulate damage in the form of a
fiber-matrix crack and an analytical model for calculating the attenuation of the waves when they pass through the
damaged region. The hybrid analysis is validated by comparison with experimental stress-strain curves and piezoelectric
sensing results for attenuation measurement. The results showed good agreement between the experimental stress-strain
curves and the results from the micromechanics analysis. Wave propagation analysis also showed good correlation
between simulation and experiment for the tested frequency range.
The effective detection and classification of damage in complex structures is an important task in the realization
of structural health monitoring (SHM) systems. Conventional information processing techniques utilize statistical
modeling machinery that requires large amounts of 'training' data which is usually difficult to obtain, leading to compromised system performance under these data-scarce conditions. However, in many SHM scenarios a modest amount of data may be available from a few different but related experiments. In this paper, a new structural damage classification method is proposed that makes use of statistics from related task(s) to improve the classification performance on a data set with limited training examples. The approach is based on the framework of transfer learning (TL) which provides a mechanism for information transfer between related
learning tasks. The utility of the proposed method is demonstrated for the classification of fatigue damage in an aluminum lug joint.
With research in structural health monitoring (SHM) moving towards increasingly complex structures for damage
interrogation, the placement of sensors is becoming a key issue in the performance of the damage detection
methodologies. For ultrasonic wave based approaches, this is especially important because of the sensitivity of the
travelling Lamb waves to material properties, geometry and boundary conditions that may obscure the presence of
damage if they are not taken into account during sensor placement. The framework proposed in this paper defines a
sensing region for a pair of piezoelectric transducers in a pitch-catch damage detection approach by taking into account
the material attenuation and probability of false alarm. Using information about the region interrogated by a sensoractuator
pair, a simulated annealing optimization framework was implemented in order to place sensors on complex
metallic geometries such that a selected minimum damage type and size could be detected with an acceptable probability
of false alarm anywhere on the structure. This approach was demonstrated on a lug joint to detect a crack and on a large
Naval SHM test bed and resulted in a placement of sensors that was able to interrogate all parts of the structure using the minimum number of transducers.
A methodology based on Lamb wave analysis and time-frequency signal processing has been developed for
damage detection and structural health monitoring of composite structures. Because the Lamb wave signals
are complex in nature, robust signal processing techniques are required to extract damage features. In this
paper, Lamb wave mode conversion is used to detect the damage in composite structures. Matching pursuit decomposition algorithm is used to represent each Lamb wave mode in the time-frequency domain. Results from numerical Lamb wave propagation simulations and experiments using orthotropic composite plate structures are presented. The capability of the proposed algorithm is demonstrated by detecting seeded delaminations in the composite plate samples. The advantages of the methodology include accurate time-frequency resolution, robustness to noise, high computational efficiency and ease of post-processing.
This paper focuses on damage detection, localization and quantification in metallic plates using an array of sensors and
an advanced feature extraction algorithm. The Matching Pursuit Decomposition (MPD) algorithm is used for timefrequency
signal analysis. Using MPD, measured signals are decomposed into multiple wave modes. The individual wave modes are analyzed to determine the cause of signal changes and the location of the damage. An aluminum plate made of 6061 alloy was instrumented with piezoelectric transducers and used for testing and validation of the proposed concept.
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.
We propose a sequential Monte Carlo (SMC) based progressive structural damage diagnosis framework that
tracks damage by integrating information from physics-based damage evolution models and using stochastic
relationships between the measurements and the damage. The approach described in this paper adaptively
configures the sensors used to collect the measurements using the minimum predicted mean squared error (MSE)
as the performance metric. Optimization is performed globally over the entire search space of all available
sensors. Results are presented for the diagnosis of fatigue damage in a notched laminate, demonstrating the
effectiveness of the proposed method.
Adaptive learning techniques have recently been considered for structural health monitoring applications due
to their flexibility and effectiveness in addressing real-world challenges such as variability in the monitoring of
environmental and operating conditions. In this paper, an active learning data selection procedure is proposed
that adaptively selects the most informative measurements to include, from multiple available measurements, in
estimating structural damage. This is important, since not all the measurements may provide useful information
and could reduce performance when processed. Within the adaptive learning framework, the data selection
problem is formulated to choose those measurements which are most representative of the diversity within a
damage state. This is achieved by extracting time-frequency analysis based statistical similarity features from
the measurements, and selecting uniformly distributed subsets to build representative reference sets. The utility
of the proposed method is demonstrated by improvements in adaptive learning performance for the estimation
of fatigue damage in an aluminum compact tension sample.
Localization of low energy impacts on carbon fiber composites is an important aspect of structural health monitoring
since it creates subsurface damage which can significantly reduce the stiffness of a component. A novel impact
localization method is proposed based on the strain amplitude measured by Fiber Bragg Grating (FBG) sensors. The
algorithm is based on the relative placement of all sensors and the maximum strain amplitude measured by each sensor.
This method requires minimal knowledge of the material or the structure and a minimum number of sensors. The
algorithm showed good results on both simulated and experimental test cases of woven composite plates. It was found
that a minimum of five FBG are necessary to accurately predict the impact location on a plate. The algorithm was also
tested on a woven composite wing showing good localization along the span of the wing but higher errors along the chord length due to the nonlinearity in the measured strains.
This work focuses on fatigue crack detection, crack tip localization and quantification in plate like structures
using a reference-free method. In many practical applications the environmental conditions in which a structure
is operated do not remain same over time. Sensor signals, thus, collected for the damaged state cannot be
compared directly with the baseline because a change in the signal can be caused by several factors other than a
structural damage. Therefore, reference-free methods are needed for damage detection. Two methods have been
discussed in this paper, one with collocated sensors and the other using matching pursuit decomposition (MPD)
to detect waves undergoing mode conversion from fatigue crack tip. The time of flight (TOF) of these mode
converted waves along with their respective velocities are further used to localize the crack tip and ultimately find
the extent of crack. Both these approaches were used to detect fatigue cracks in aluminum plates made of 6061
alloy. These samples were instrumented with collocated piezoelectric sensors and tested under constant amplitude
fatigue loading. Crack tip localization was done from the TOF information extracted for mode converted waves
using MPD. The crack lengths obtained using this reference-free technique were validated with experimental crack lengths and were found to be in good agreement.
Automated detection of damage due to impact in composite structures is very important for aerospace structural health
monitoring (SHM) applications. Fiber Bragg grating (FBG) sensors show promise in aerospace applications since they
are immune to electromagnetic interference and can support multiple sensors in a single fiber. However, since they only
measure strain along the length of the fiber, a prediction scheme that can estimate loading using randomly oriented
sensors is key to damage state awareness. This paper focuses on the prediction of impact loading in composite structures
as a function of time using a support vector regression (SVR) approach. A time delay embedding feature extraction
scheme is used since it can characterize the dynamics of the impact using the sensor signal from the FBGs. The
efficiency of this approach has been demonstrated on simulated composite plates and wing structures. Training with
impacts at four locations with three different energies, the constructed framework is able to predict the force-time history
at an unknown impact location to within 12 percent on the composite plate and to within 10 percent on a composite wing when the impact was within the sensor network region.
Fatigue damage sensing and measurement in aluminum alloys is critical to estimating the residual useful lifetime of a
range of aircraft structural components. In this work, we present electrical impedance and ultrasonic measurements in
aluminum alloy 2024 that has been fatigued under high cycle conditions. While ultrasonic measurements can indicate
fatigue-induced damage through changes in stiffness, the primary indicator is ultrasonic attenuation. We have used laser
ultrasonic methods to investigate changes in ultrasonic attenuation since simultaneous measurement of longitudinal and
shear properties provides opportunities to develop classification algorithms that can estimate the degree of damage.
Electrical impedance measurements are sensitive to changes in the conductivity and permittivity of materials - both are
affected by the microstructural damage processes related to fatigue. By employing spectral analysis of impedance over a
range of frequencies, resonance peaks can be identified that directly reflect the damage state in the material. In order to
compare the impedance and ultrasonic measurements for samples subjected to tension testing, we use processing and
classification tools that are matched to the time-varying spectral nature of the measurements. Specifically, we process
the measurements to extract time-frequency features and estimate stochastic variation properties to be used in robust
classification algorithms. Results are presented for fatigue damage identification in aluminum lug joint specimens.
Woven fiber composites are currently being investigated due to their advantages over other materials, making them
suitable for low weight, high stiffness, and high interlaminar fracture toughness applications such as missiles, body
armor, satellites, and many other aerospace applications. Damage characterization of woven fabrics is a complex task
due to their tendency to exhibit different failure modes based on the weave configuration, orientation, ply stacking and
other variables. A multiscale model is necessary to accurately predict progressive damage. The present research is an
experimental study on damage characterization of three different woven fiber laminates under low energy impact using
Fiber Bragg Grating (FBG) sensors and flash thermography. A correlation between the measured strain from FBG
sensors and the damaged area obtained from flash thermography imaging has been developed. It was observed that the
peak strain in the fabrics were strongly dependent on the weave geometry and decreased at different rates as damage area
increased due to dissimilar failure modes. Experimental observations were validated with the development of a
multiscale model. A FBG sensor placement model was developed which showed that FBG sensor location and
orientation plays a key role in the sensing capabilities of strain on the samples.
The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications.
Many of the candidate algorithms for these applications either require a lot of training examples or are very
computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a
combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a
computationally efficient classification scheme for rapid damage state determination. LDA was used for feature
extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the
SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that
needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification,
which also reduces the total training time required. This framework was validated on composite plates that were
impacted at various locations. The results show that the algorithm was able to correctly predict the different impact
damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.
A procedure to monitor crack growth in Aluminum lug joints subject to fatigue loading is developed. Sensitivity
analysis is used to decide sensor importance and monitor crack growth rate. A new feature extraction technique
based on Discrete Cosine Transformation (DCT) is developed to analyze complex sensor signals. Self-sensing
piezoelectric sensors are surface mounted on Al 2024 T351 lug joint samples, 0.25 in. thickness. Samples with
single crack site and multiple crack sites were used in this study and to initiate multiple crack sites, they were
notched symmetrically near the shoulders and then tested under a fatigue load of 110lbs (0.49kN) to 1100lbs
(4.9kN). Crack lengths were monitored over the entire life of the lug joint sample using a CCD camera. Active
sensing was carried out at every crack length, when the machined was stopped. The piezoelectric actuator was
excited with a chirp signal, swept between 1kHz to 500kHz, and sensor readings were collected at a sampling rate
of 2Ms/s. Using three different sensor sensitivity algorithms, the sensor signals are analyzed and their efficiency
in predicting crack growth rates and deciding sensor importance is studied. Sensor sensitivity is defined as the
changes observed in sensor signals obtained from a damaged sample compared to healthy sample. The first
two algorithms, ORCA and One-Class SVM's, are based on statistical techniques for outlier detection and the
third algorithm, a new detection framework, is based on feature extraction using Discrete Cosine Transformation
(DCT). The efficacy of these methods for damage characterization is presented.
Prognostic algorithms indicate the remaining useful life based on fault detection and diagnosis through condition
monitoring framework. Due to the wide-spread applications of advanced composite materials in industry, the
importance of prognosis on composite materials is being acknowledged by the research community. Prognosis has
the potential to significantly enhance structural monitoring and maintenance planning. In this paper, a Gaussian
process based prognostics framework is presented. Both off-line and on-line methods combined state estimation and
life prediction of composite beam subject to fatigue loading. The framework consists of three main steps: 1) data
acquisition, 2) feature extraction, 3) damage state prediction and remaining useful life estimation. Active
piezoelectric and acoustic emission (AE) sensing techniques are applied to monitor the damage states. Wavelet
transform is used to extract the piezoelectric sensing features. The number of counts from AE system was used as a
feature. Piezoelectric or AE sensing features are used to build the input and output space of the Gaussian process.
The future damage states and remaining useful life are predicted by Gaussian process based off-line and on-line
algorithms. Accuracy of the Gaussian process based prognosis method is improved by including more training sets.
Piezoelectric and AE features are also used for the state prediction. In the test cases presented, the piezoelectric
features lead to better prognosis results. On-line prognosis is completed sequentially by combining experimental and
predicted features. On-line damage state prediction and remaining useful life estimation shows good correlation with
experimental data at later stages of fatigue life.
Carbon-fiber composites will increasingly be used in next generation air transportation vehicles. Therefore, it is critical
to develop state awareness models that can accurately capture the damage states and predict remaining useful life based
on current and future loading conditions. In the current research, a structural health monitoring (SHM) and prognosis
framework is being developed for heterogeneous material systems. The objective of this paper is to present some of the
experimental components of this work. In the experiments preformed, the use of a pitch catch method using
piezoelectric transducers for both the actuator and sensor were employed for collecting information on the damage
status. The focus of this work is to quantify damage within the sample by relating parameters in the sensor signal to
damage intensity. Good correlation has been observed in several tests between damage level and wave attenuation.
These results are confirmed using off-the-shelf NDE techniques.
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.
We describe a statistical method for the classification of damage in complex structures. Our approach is based
on a Bayesian framework using hidden Markov models (HMMs) to model time-frequency features extracted from
structural data. We also propose two different methods for sensor fusion to combine information from multiple
distributed sensors such that the overall classification performance is increased. The proposed approaches are
applied to the classification and localization of delamination in a laminated composite plate. Results using
both discrete and continuous observation density HMMs, together with the sensor fusion, are presented and
discussed.
KEYWORDS: Signal to noise ratio, Data modeling, Time-frequency analysis, 3D modeling, Interference (communication), Finite element methods, Physics, Chemical species, Performance modeling, Sensors
We have recently proposed a method for classifying waveforms from healthy and damaged structures in a structural
health monitoring framework. This method is based on the use of hidden Markov models with preselected
feature vectors obtained from the time-frequency based matching pursuit decomposition. In order to investigate
the performance of the classifier for different signal-to-noise ratios (SNR), we simulate the response of a lug joint
sample with different crack lengths using finite element modeling (FEM). Unlike experimental noisy data, the
modeled data is noise free. As a result, different levels of noise can be added to the modeled data in order to
obtain the true performance of the classifier under additive white Gaussian noise. We use the finite element
package ABAQUS to simulate a lug joint sample with different crack lengths and piezoelectric sensor signals.
A mesoscale internal state variable damage model defines the progressive damage and is incorporated in the
macroscale model. We furthermore use a hybrid method (boundary element-finite element method) to model
wave reflection as well as mode conversion of the Lamb waves from the free edges and scattering of the waves
from the internal defects. The hybrid method simplifies the modeling problem and provides better performance
in the analysis of high stress gradient problems.
Research is being conducted in damage diagnosis and prognosis to develop state awareness models and residual useful
life estimates of aerospace structures. This work describes a methodology using Support Vector Machines (SVMs),
organized in a binary tree structure to classify the extent of a growing crack in lug joints. A lug joint is a common
aerospace 'hotspot' where fatigue damage is highly probable. The test specimen was instrumented with surface mounted
piezoelectric transducers and then subjected to fatigue load until failure. A Matching Pursuit Decomposition (MPD)
algorithm was used to preprocess the sensor data and extract the input vectors used in classification. The results of this
classification scheme show that this type of architecture works well for categorizing fatigue induced damage (crack) in a
computationally efficient manner. However, due to the nature of the overlap of the collected data patterns, a classifier at
each node in the binary tree is limited by the performance of the classifier that is higher up in the tree.
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.
This paper presents the use of guided wave concept in localizing small cracks in complex lug joint structures. A lug joint
is a one of the several 'hotspots' in an aerospace structure which experiences fatigue damage. Several fatigue tests on lug
joint samples prepared from 0.25" plate of Aluminum (Al) 2024 T351 indicated a distinct failure pattern. All samples
failed at the shoulders. Different notch sizes are introduced at the shoulders and both virtual and real active health
monitoring with piezoelectric transducers is performed. Simulations of the real time experiment are carried out using
Finite Element (FE) analysis. Similar crack geometry and piezoelectric transducer orientation are considered both in
experiment and in simulation. Results presented illustrate the use of guided waves in interrogating damage in lug joints.
A comparison of sensor signals has been made between experimental and simulated signals which show good
correlation. The frequency transform on the sensor signal data yield useful information for characterizing damage.
Further, sensitivity studies are performed. The sensitivity study information offers potential application in reducing the
computational cost for any defect localization technique by reducing redundant sensors. This information is a key to
optimal sensor placement for damage detection in structural health monitoring (SHM).
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.
The ability to detect and classify damages in complex materials and structures is an important problem from
both safety and economical perspectives. This paper develops a novel approach based on Hidden Markov Models
(HMMs) for the classification of structural damage. Our approach here is based on using HMMs for modeling
the time-frequency features extracted from time-varying structural data. Unlike conventional deterministic
methods, the HMM is a stochastic approach which better accounts for the uncertainties encountered in the
structural problem and leads to a more robust health monitoring system. The utility of the proposed approach
is demonstrated via example results for the classification of fastener damage in an aluminum plate.
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.
A new technique to characterize localized linear elastic constitutive behavior within a Fiber Optic Sensor (FOS) embedded parallel to reinforcing fibers has been developed. A unit cell model has been developed with stresses anywhere within the unit cell formulated as a function of sensor strains. A slicing approach has been implemented within the unit cell to determine effective stresses within each slice of the unit cell. Different layers of the FOS are modeled using individual slices to model the stresses within a given slice. Numerical results are presented for SMF-28 FOS. The accuracy of the developed slicing based micromechanics approach has been validated by comparisons with results obtained using an established micromechanics analysis code based on the Generalized Method of Cells and a general purpose finite element technique. Effective unit cell stress results from all three models show close correlation to one another. In addition, convergence of normal stresses was also investigated with increasing volume fraction of surrounding reinforcing carbon fibers.
The present research investigates the complex phenomena of wave scattering in bolted joint. The goal is to develop an understanding of the attenuation behavior of propagating waves, through the structure, as the bolt is subjected to different torques. This is a first step towards developing a structural health monitoring technique for detecting torque loss at a bolted joint. To simulate the local effects of the bolt, a micromechanics based model has been developed to model the scattering and attenuation behavior due to a single fiber in a matrix with a circumferential interface crack. A slicing approach is used to account for the effect of multiple interfacial cracks at different orientations through the depth
of the structure, to simulate the global effects of the bolt. The change in wave attenuation as a function of bolt location, at different depths in a plate, is studied. Next, the variation in wave scattering as the bolt, which is now fully embedded in the plate, is subjected to different torque is investigated. The local stress fields that develop in the plate due to the torque are treated as a pre-stress condition and their effect on the resultant wave scattering is investigated using the developed model. The resultant attenuation accounts for the combined effect of the geometrical attenuation and the attenuation due to the pre-stress. Numerical results obtained show small but steady increase in the attenuation with the applied torque. Experiments conducted to validate the developed model show similar trends.
In structural health monitoring, energy dissipation of wave propagation is a key factor to determine optimal placement of sensors and quantify damage. This paper focuses on the study of wave scattering and attenuation in fiber-reinforced composite laminates with damage. In order to obtain the overall attenuation coefficient, the propagation of elastic shear wave in fiber-matrix medium is investigated starting from the Helmholtz equation. The wave attenuation due to interfacial damage is considered. The attenuation due to cracks of varying sizes and the effect of frequency on the attenuation value has been examined. It can be shown that a critical frequency exists at a given crack size for which the attenuation in the composite medium is at its highest value. Furthermore, the wave attenuation in composite laminates is investigated by incorporating energy transfer in layerwise medium. The overall attenuation coefficient for the laminate is obtained. Experiments are also conducted to evaluate some of the observations obtained from the model.
In structural health monitoring, the fundamental goal is to address the problem of damage identification, localization and quantification. Using the wave based approach, the presence of damage is visualized in terms of the changes in the signature of the resultant wave that propagates through the structure. Since surface mounted piezoelectric transducers have been used for monitoring, the voltage output of each sensor is used for signature characterization. Due to the time-varying nature of these signals, performance of some existing analyzing tools may not be satisfactory. In the present study, the use of the matching pursuit decomposition has been investigated as a signal processing technique to compare signals from healthy and damaged structures.
A new improved nonlinear transient generalized layerwise theory for modeling embedded discrete and continuous sensor(s) outputs in laminated composite plates with acoustic emission from cracks and embedded delaminations is developed. The computational modeling involves development of a finite element scheme using an improved layerwise laminate theory for a composite laminate plate with embedded discrete and continuous sensors and embedded discrete delaminations. The simulated cases studied included cantilever plates with embedded sensors and embedded delamination under low frequency vibration and square plates with discrete embedded sensors and continuous embedded sensor architecture and embedded discrete delaminations under high frequency acoustic emission. The effect on sensor outputs due to scattering of the acoustic emission due to the presence of delamination is also investigated. It is expected that this analytical model would be a useful tool for numerical simulation of composite laminated structures with embedded delaminations and embedded sensor architecture, particularly since experimental investigation could often be prohibitive to simulate different conditions.
Modern combat aircraft are required to achieve aggressive maneuverability and high agility performance, while maintaining handling qualities over a wide range of flight conditions. Recently, a new adaptive-structural concept called variable stiffness spar is proposed in order to increase the maneuverability of the flexible aircraft. The variable stiffness spar controls wing torsional stiffness to enhance roll performance in the complete flight envelope. However, variable stiffness spar requires the mechanical actuation system in order to rotate the Variable stiffness spar during flight. The mechanical actuation system to rotate variable stiffness spar may cause an additional weight increase. In this paper, we will apply Shape Memory Alloy (SMA) spars for aeroelastic performance enhancement. In order to explore the potential of SMA spar design, roll performance of the composite smart wings will be investigated using ASTROS. Parametric study will be conducted to investigate the SMA spar effects by changing the spar locations and geometry. The results show that with activation of the SMA spar, the roll effectiveness can be increased up to 61% compared with the baseline model.
Health monitoring of aerospace structures can be done passively by listening for acoustic waves generated by cracks, impact damage and delaminations, or actively by propagating diagnostic stress waves and interpreting the parameters that characterize the wave travel. This paper investigates modeling of flexural wave propagation in a plate and the design of sensors to detect damage in plates based on stress wave parameters. To increase understanding of the actual physical process of wave propagation, a simple model is developed to simulate wave propagation in a plate with boundaries. The waves can be simulated by applied forces and moments in the model either to represent passive damage growth or active wave generation using piezoceramic actuators. For active wave generation, the model considers a piezoceramic patch bonded perfectly to a quasi-isotropic glass-epoxy composite plate. Distributed sensors are used on the plate and are modeled as being constructed using active fiber composite and piezoceramic materials. For active wave generation, a moment impulse is generated by the actuation of a piezoceramic patch. The waves generated from the patch are detected by the distributed sensor. For passive sensing of acoustic waves, a step function is used to simulate an acoustic emission from a propagating damage. The resulting acoustic wave is measured by the distributed sensor and produces micro-strains in the sensor nodes. The strains produce a single voltage signal output from the distributed sensor. Computational simulations and animations of acoustic wave propagation in a plate are discussed in the paper. A new method to locate the source of an acoustic emission using the time history of the dominant lower frequency components of the flexural wave mode detected by continuous sensors is also presented.
The concept of smart structures, such as piezoelectric laminates, has received a great deal of attention recently as an alternative to conventional techniques. These advanced structures can be designed to actively react to disturbance forces in order to maintain structural integrity while maintaining, or even improving, the level of performance. Great potential can be found in advanced aerospace structural applications. However, the introduction of smart devices inevitably perturb the local values of the field variables and nucleate damage such as debonding and delamination at the interface of piezoelectric devices and the host structure due to stress concentration. The layerwise characteristics of the laminates make the determination of stress and strain distribution a challenging problem. Conventionally, classical lamination theory has been extended to smart laminated structures which ignores transverse shear effects'3. A higher order theory was proposed and applied by Chattopadhyay et al.4'5 in the analysis oflaminated structures to address transverse shear effects without shear correction factors. The theory proved to be successful in global analysis for thick structures and smart structures. However, it fails to provide continuous distribution of transverse shear stresses. This implies that the theory is not sufficient in predicting local information regarding stress and strain distributions which is critical in the analysis of structural failure. The multifield characteristics of piezoelectric structures make the analysis even more complex, particularly in the presence of thermal effects as dictated by specific missions. A typical environment is represented by a solar flux of 1350W/m2 as vehicles move from shadow to sunlight. Some research in the field of smart structural modeling in the presence of thermal effects has been reported610. However, oneway coupling that only considers the effect of a known field on another field is used in these works. The bi-way coupling between piezoelectric and mechanical fields was included in the hybrid plate theory developed by Mitchell and Reddy3. A coupled thermal-piezoelectric-mechanical (t-p-m) model was developed by Chattopadhyay et al.1113 to address the bi-way coupling issues associated with smart composites under thermal loads. Their work indicates that the effects of bi-way coupling on structural deformation increase with the thickness of piezoelectric device. However, an equivalent single layer approach is used, and therefore the localized interlaminar characteristics cannot be addressed accurately by this theory. The present paper aims at the investigation of interlaminar stress distribution in laminated shell structures using coupled thermal-piezoelectric-mechanical model. The goal is to develop a theory that is capable of providing sufficient accuracy while guaranteeing computational efficiency compared to other layerwise theories. To maintain local accuracy of stress and strain distributions, the trial displacement field is assumed using zigzag functions and C0 continuity through the entire laminate thickness accommodating zigzag in-plane warping and interlaminar transverse shear stress continuity. The continuity conditions of inplane displacement and transverse shear stress fields as well as traction free boundary conditions are applied to reduce the number of primary structural variables. The temperature and electrical fields are assumed using higher order functions. These descriptions can satisfy surface boundary conditions of heat flux and electrical potential. The mathematical model is implemented using finite element technique. The case of cylindrical bending and spherical composite shell structures with piezoelectric patches are investigated. The analysis of stress distributions under electrical and thermoelectrical loading is performed and numerical results are presented.
A smart structural model is developed to analytically determine the response of arbitrary structures with piezoelectric materials and attached electrical circuitry. The equations of motion are formulated using the coupled piezoelectric formulations. However, rather than solving for strain and electric field, the proposed model solves for the strain and electric charge. The equations of motion are simplified for the case of a composite plate structure using a refined higher order laminate theory. Additional degrees of freedom are then added to describe any attached electrical circuitry. A method is also presented for system simplification using the structural mode shapes and natural frequencies. Results are verified using experimental data for passive electrical shunt damping. The developed model results in a general framework that can be useful in solving a wide variety of coupled piezoelectric-mechanical problems addressing issues such as passive electrical damping, self-sensing and electrical power consumption.
A new theory is developed to model the hysteresis relation between polarization and electric field of piezoceramics. An explicit formulation governing the hysteresis is obtained by using a saturation polarization, remnant polarization and coercive electric field. A new form of elastic Gibbs energy is proposed to address the coupling relations between electrical field and mechanical field. The nonlinear constitutive relations are derived from the elastic Gibbs energy and are applicable in the case of high stroke actuation. The hysteresis relations obtained using the current model are correlated with experimental results. The static deflection of a cantilever beam with surface-bonded piezoelectric actuators is analyzed by implementing the current constitutive relations. Numerical results reveal that hysteresis is an important issue in the application of piezoceramics.
Aeromechanical stability plays a critical role in helicopter design and lead-lag damping is crucial to this design. In this paper, the use of segmented constrained damping layer (SCL) treatment and composite tailoring is investigated for improved rotor aeromechanical stability using formal optimization technique. The principal load-carrying member in the rotor blade is represented by a composite box beam, of arbitrary thickness, with surface bonded SCLs. A comprehensive theory is used to model the smart box beam. A ground resonance analysis model and an air resonance analysis model are implemented in the rotor blade built around the composite box beam with SCLs. The Pitt-Peters dynamic inflow model is used in air resonance analysis under hover condition. A hybrid optimization technique is used to investigate the optimum design of the composite box beam with surface bonded SCLs for improved damping characteristics. Parameters such as stacking sequence of the composite laminates and placement of SCLs are used as design variables. Detailed numerical studies are presented for aeromechanical stability analysis. It is shown that optimum blade design yields significant increase in rotor lead-lag regressive modal damping compared to the initial system.
KEYWORDS: Neural networks, Ferroelectric materials, Actuators, Smart structures, Control systems, Aerodynamics, Control systems design, Shape memory alloys, Matrices, Adaptive control
Recent development of a smart structures module and its successful integration with a multidisciplinary design optimization software ASTROS* and an Aeroservoelasticity (ASE) module is presented. A modeled F-16 wing using piezoelectric (PZT) actuators was used as an example to demonstrate the integrated software capability to design a flutter suppression system. For an active control design, neural network based robust controller will be used for this study. A smart structures module is developed by modifying the existing thermal loads module in ASTROS* in order to include the effects of the induced strain due to piezoelectric (PZT) actuation. The thermal-PZT equivalence model enables the modifications of the thermal stress module to accommodate the smart structures module in ASTROS*. ZONA developed the control surface (CS)/PZT equivalence model principle, which ensures the interchangeability between the CS force input and the PZT force input to the ASE modules in ASTROS*. The results show that the neural net based controller can increase the flutter speed.
KEYWORDS: Composites, Control systems design, Vibration control, Structural design, Systems modeling, Aerospace engineering, Control systems, Integration, Chemical elements, Matrices
A rigorous multi-objective optimization procedure, is developed to address the integrated structures/control design of composite plates with surface bonded segmented active constrained layer (ACL) damping treatment. The Kresselmeier- Steinhauser function approach is used to formulate this multidisciplinary problem. The goal is to control vibration without incorporating a weight penalty. Objective functions and constraints include damping ratios, structural weight and natural frequencies. Design variables include the ply stacking sequence, dimensions and placement of segmented ACL. The optimal designs show improved plate vibratory characteristics and reduced structural weight. The results of the multi- objective optimization problem are compared to those of a single objective optimization with vibration control as the objective. Results establish the necessity for developing the integrated structures/controls optimization procedure.
KEYWORDS: Actuators, Ferroelectric materials, Composites, Control systems, Matrices, Vibration control, Signal attenuation, Smart materials, System integration, Control systems design
An integrated structures/controls optimization is developed for vibration suppression of a composite box beam with surface bonded piezoelectric actuators. The penalty approach is used to perform the multi-objective hybrid optimization to enhance damping of the first lag, flap, and torsion modes while minimizing control input. The objective functions and constraints include damping ratios, and natural frequencies. The design variables include ply orientations of the box beam walls, and the location and size of the actuators. Two box beam configurations are investigated and the results are compared. In the first, piezoelectric actuators are bonded to the top and bottom surfaces and in the second, actuators are bonded to all four walls for additional in-plane actuation. Optimization results show that significant reductions in control input and tip displacement can be achieved in both cases, however, improved response trends are obtained with in- plane actuation.
KEYWORDS: Composites, Control systems design, Actuators, Aerodynamics, Control systems, Systems modeling, Motion models, Interfaces, Finite element methods, Matrices
In this paper, aeroelastic performance of smart composite wing in the presence of delaminations is investigated. A control system is designed to enhance the dynamic stability of the delaminated composite wing. The refined higher order theory for analyzing an adaptive composite plate in the presence of delaminations is used. The theory accurately captures the transverse shear deformation through the thickness, which is important in anisotropic composites, particularly in the presence of discrete actuators and sensors and delaminations. The effects of delamination on the aeroelastic characteristics of composite plates are investigated. An active control system is designed to redistribute the loads and to minimize the effect of delamination. The pole placement technique is applied to design the closed loop system by utilizing piezoelectric actuators. Due to delamination, the significant changes in the natural frequencies of the lower modes are observed. And this causes the reduction on the flutter speed of the delaminated plate model. The aeroelastic control results show that controller makes the delaminated plate model behave like a normal plate. The controller also reduces a significant amount of RMS values of the gust response due to gust.
A new class of bimorph actuators, called C-block actuators for their curved shape, have recently been proposed to provide improved performance characteristics over conventional straight bimorph actuators. Existing mathematical models of these actuators are based on classical curved beam theory which neglects transverse shear effects. An improved mathematical model for C-block actuators of arbitrary thickness is developed. The first order shear deformation based theory model accounts for through-the-thickness transverse shear stresses in thick piezoelectric C-block actuators. The results obtained from the first order shear deformation theory are validated with results from finite element analysis, available experimental data and an exact elasticity solution. The developed theory is used to predict the performance of multilayered C-block actuators of various configurations to demonstrate the importance of shear effects.
Active control of fixed wing aircraft using piezoelectric materials has the potential to improve its aeroelastic response while reducing weight penalties. However, the design of active aircraft wings is a complex optimization problem requiring the use of formal optimization techniques. In this paper, a hybrid optimization procedure is applied to the design of an airplane wing, represented by a flat composite plate, with piezoelectric actuation to improve the aeroelastic response. Design objectives include reduced static displacements, improved passenger comfort during gust and increased damping. Constraints are imposed on the electric power consumption and ply stresses. Design variables include composite stacking sequence, actuator/sensor locations and controller gain. Numerical results indicate significant improvements in the design objectives and physically meaningful optimal designs.
A new class of bimorph actuators, called C-block actuators for their curved shape, have recently been proposed to provide improved performance characteristics over conventional straight bimorph actuators. Existing mathematical models of these actuators are based on classical curved beam theory which neglects transverse shear effects. The paper presents an improved mathematical model for C-block actuators of arbitrary thickness. The new model accounts for through-the-thickness transverse shear deformations using refined displacement fields. The results from this theory are compared to the existing classical model and experimental data. Both free and forced end conditions are investigated. An exact elasticity solution is also developed which provides a framework for comparison of the developed theory and other approximate theories.
The concept of a rotor blade with a smart flap has received considerable attention lately due to the potential for vibration suppression using individual blade control (IBC). In this paper curved polymeric piezoelectric actuators, called C-block actuators, which exhibit significant advantages over other types of actuators are proposed to drive a smart flap for IBC. The efficient implementation involves the design of both the actuators and the flap. Therefore, it is appropriate to use a formal optimization technique to address this problem. The optimization problem is complex since it includes both continuous (flap size) and discrete (number of actuators) design variables. Therefore, a newly developed hybrid optimization procedure, which can include both types of design variables, is used to maximize flap performance using the C-block actuators. Optimization results indicate that the C-block actuators provide comparable control authority without the drawbacks, such as brittleness, of conventional bimorph actuators.
A refined higher order plate theory is developed to investigate the actuation mechanism of piezoelectric materials surface bonded or embedded in composite laminates. The current analysis uses a displacement field which accurately accounts for transverse shear stresses. Some higher order terms are identified by using the conditions that shear stresses vanish at all free surfaces. Therefore, all boundary conditions for displacements and stresses are satisfied in the present theory. The analysis is implemented using the finite element method which provides a convenient means to construct a numerical solution due to the discrete nature of the actuators. The higher order theory is computationally less expensive than a full 3D analysis. The theory is also shown to agree well with published experimental results. Numerical examples are presented for composite plates with thickness ranging from thin to very thick.
A multiobjective optimization procedure is developed for rotating composite box beams with discrete piezoelectric actuators. A rotating composite cantilever box beam model is presented that includes piezoelectric elements used as induced-strain actuators for vibration control. The model is implemented using the finite elements method. Multiple design objectives are efficiently combined using a multiobjective optimization formulation. Actuator locations and ply-stacking sequences are represented with discrete (0,1) variables while structural/control parameters such as box beam dimensions are continuous design variables. A transformation technique is used to formulate the combined continuous/discrete problem. This allows both optimal actuator locations and structural/control parameters to be determined inside a closed loop procedure. A technique based on simulated annealing is used for optimization in conjunction with an approximate analysis technique to reduce computational effort.
Active vibration control of structures using piezoelectric materials is a new approach for damping unwanted vibrations in structures lacking sufficient stiffness or passive damping. The finite elements method is used to model active damping elements which are piezoelectric actuators bonded to a box beam. Efficient implementation of these actuators requires that their optimal locations on the structure be determined and that the structure be designed to best utilize the properties of the piezoelectrics. A formal optimization procedure has been developed to address both of these issues. Multiobjective optimization techniques are used to minimize multiple and conflicting design objectives such as mass and energy dissipated by the piezoelectric actuators.
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