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- All Subjects: Mechanical Engineering
- Creators: Chattopadhyay, Aditi
Description
Composite materials are increasingly being used in aircraft, automobiles, and other applications due to their high strength to weight and stiffness to weight ratios. However, the presence of damage, such as delamination or matrix cracks, can significantly compromise the performance of these materials and result in premature failure. Structural components are often manually inspected to detect the presence of damage. This technique, known as schedule based maintenance, however, is expensive, time-consuming, and often limited to easily accessible structural elements. Therefore, there is an increased demand for robust and efficient Structural Health Monitoring (SHM) techniques that can be used for Condition Based Monitoring, which is the method in which structural components are inspected based upon damage metrics as opposed to flight hours. SHM relies on in situ frameworks for detecting early signs of damage in exposed and unexposed structural elements, offering not only reduced number of schedule based inspections, but also providing better useful life estimates. SHM frameworks require the development of different sensing technologies, algorithms, and procedures to detect, localize, quantify, characterize, as well as assess overall damage in aerospace structures so that strong estimations in the remaining useful life can be determined. The use of piezoelectric transducers along with guided Lamb waves is a method that has received considerable attention due to the weight, cost, and function of the systems based on these elements. The research in this thesis investigates the ability of Lamb waves to detect damage in feature dense anisotropic composite panels. Most current research negates the effects of experimental variability by performing tests on structurally simple isotropic plates that are used as a baseline and damaged specimen. However, in actual applications, variability cannot be negated, and therefore there is a need to research the effects of complex sample geometries, environmental operating conditions, and the effects of variability in material properties. This research is based on experiments conducted on a single blade-stiffened anisotropic composite panel that localizes delamination damage caused by impact. The overall goal was to utilize a correlative approach that used only the damage feature produced by the delamination as the damage index. This approach was adopted because it offered a simplistic way to determine the existence and location of damage without having to conduct a more complex wave propagation analysis or having to take into account the geometric complexities of the test specimen. Results showed that even in a complex structure, if the damage feature can be extracted and measured, then an appropriate damage index can be associated to it and the location of the damage can be inferred using a dense sensor array. The second experiment presented in this research studies the effects of temperature on damage detection when using one test specimen for a benchmark data set and another for damage data collection. This expands the previous experiment into exploring not only the effects of variable temperature, but also the effects of high experimental variability. Results from this work show that the damage feature in the data is not only extractable at higher temperatures, but that the data from one panel at one temperature can be directly compared to another panel at another temperature for baseline comparison due to linearity of the collected data.
ContributorsVizzini, Anthony James, II (Author) / Chattopadhyay, Aditi (Thesis advisor) / Fard, Masoud (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
Multiaxial mechanical fatigue of heterogeneous materials has been a significant cause of concern in the aerospace, civil and automobile industries for decades, limiting the service life of structural components while increasing time and costs associated with inspection and maintenance. Fiber reinforced composites and light-weight aluminum alloys are widely used in aerospace structures that require high specific strength and fatigue resistance. However, studying the fundamental crack growth behavior at the micro- and macroscale as a function of loading history is essential to accurately predict the residual fatigue life of components and achieve damage tolerant designs. The issue of mechanical fatigue can be tackled by developing reliable in-situ damage quantification methodologies and by comprehensively understanding fatigue damage mechanisms under a variety of complex loading conditions. Although a multitude of uniaxial fatigue loading studies have been conducted on light-weight metallic materials and composites, many service failures occur from components being subjected to variable amplitude, mixed-mode multiaxial fatigue loadings. In this research, a systematic approach is undertaken to address the issue of fatigue damage evolution in aerospace materials by:
(i) Comprehensive investigation of micro- and macroscale crack growth behavior in aerospace grade Al 7075 T651 alloy under complex biaxial fatigue loading conditions. The effects of variable amplitude biaxial loading on crack growth characteristics such as crack acceleration and retardation were studied in detail by exclusively analyzing the influence of individual mode-I, mixed-mode and mode-II overload and underload fatigue cycles in an otherwise constant amplitude mode-I baseline load spectrum. The micromechanisms governing crack growth behavior under the complex biaxial loading conditions were identified and correlated with the crack growth behavior and fracture surface morphology through quantitative fractography.
(ii) Development of novel multifunctional nanocomposite materials with improved fatigue resistance and in-situ fatigue damage detection and quantification capabilities. A state-of-the-art processing method was developed for producing sizable carbon nanotube (CNT) membranes for multifunctional composites. The CNT membranes were embedded in glass fiber laminates and in-situ strain sensing and damage quantification was achieved by exploiting the piezoresistive property of the CNT membrane. In addition, improved resistance to fatigue crack growth was observed due to the embedded CNT membrane.
(i) Comprehensive investigation of micro- and macroscale crack growth behavior in aerospace grade Al 7075 T651 alloy under complex biaxial fatigue loading conditions. The effects of variable amplitude biaxial loading on crack growth characteristics such as crack acceleration and retardation were studied in detail by exclusively analyzing the influence of individual mode-I, mixed-mode and mode-II overload and underload fatigue cycles in an otherwise constant amplitude mode-I baseline load spectrum. The micromechanisms governing crack growth behavior under the complex biaxial loading conditions were identified and correlated with the crack growth behavior and fracture surface morphology through quantitative fractography.
(ii) Development of novel multifunctional nanocomposite materials with improved fatigue resistance and in-situ fatigue damage detection and quantification capabilities. A state-of-the-art processing method was developed for producing sizable carbon nanotube (CNT) membranes for multifunctional composites. The CNT membranes were embedded in glass fiber laminates and in-situ strain sensing and damage quantification was achieved by exploiting the piezoresistive property of the CNT membrane. In addition, improved resistance to fatigue crack growth was observed due to the embedded CNT membrane.
ContributorsDatta, Siddhant (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Jiang, Hanqing (Committee member) / Marvi, Hamidreza (Committee member) / Tang, Pingbo (Committee member) / Yekani Fard, Masoud (Committee member) / Iyyer, Nagaraja (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation presents the development of structural health monitoring and prognostic health management methodologies for complex structures and systems in the field of mechanical engineering. To overcome various challenges historically associated with complex structures and systems such as complicated sensing mechanisms, noisy information, and large-size datasets, a hybrid monitoring framework comprising of solid mechanics concepts and data mining technologies is developed. In such a framework, the solid mechanics simulations provide additional intuitions to data mining techniques reducing the dependence of accuracy on the training set, while the data mining approaches fuse and interpret information from the targeted system enabling the capability for real-time monitoring with efficient computation.
In the case of structural health monitoring, ultrasonic guided waves are utilized for damage identification and localization in complex composite structures. Signal processing and data mining techniques are integrated into the damage localization framework, and the converted wave modes, which are induced by the thickness variation due to the presence of delamination, are used as damage indicators. This framework has been validated through experiments and has shown sufficient accuracy in locating delamination in X-COR sandwich composites without the need of baseline information. Besides the localization of internal damage, the Gaussian process machine learning technique is integrated with finite element method as an online-offline prediction model to predict crack propagation with overloads under biaxial loading conditions; such a probabilistic prognosis model, with limited number of training examples, has shown increased accuracy over state-of-the-art techniques in predicting crack retardation behaviors induced by overloads. In the case of system level management, a monitoring framework built using a multivariate Gaussian model as basis is developed to evaluate the anomalous condition of commercial aircrafts. This method has been validated using commercial airline data and has shown high sensitivity to variations in aircraft dynamics and pilot operations. Moreover, this framework was also tested on simulated aircraft faults and its feasibility for real-time monitoring was demonstrated with sufficient computation efficiency.
This research is expected to serve as a practical addition to the existing literature while possessing the potential to be adopted in realistic engineering applications.
In the case of structural health monitoring, ultrasonic guided waves are utilized for damage identification and localization in complex composite structures. Signal processing and data mining techniques are integrated into the damage localization framework, and the converted wave modes, which are induced by the thickness variation due to the presence of delamination, are used as damage indicators. This framework has been validated through experiments and has shown sufficient accuracy in locating delamination in X-COR sandwich composites without the need of baseline information. Besides the localization of internal damage, the Gaussian process machine learning technique is integrated with finite element method as an online-offline prediction model to predict crack propagation with overloads under biaxial loading conditions; such a probabilistic prognosis model, with limited number of training examples, has shown increased accuracy over state-of-the-art techniques in predicting crack retardation behaviors induced by overloads. In the case of system level management, a monitoring framework built using a multivariate Gaussian model as basis is developed to evaluate the anomalous condition of commercial aircrafts. This method has been validated using commercial airline data and has shown high sensitivity to variations in aircraft dynamics and pilot operations. Moreover, this framework was also tested on simulated aircraft faults and its feasibility for real-time monitoring was demonstrated with sufficient computation efficiency.
This research is expected to serve as a practical addition to the existing literature while possessing the potential to be adopted in realistic engineering applications.
ContributorsLi, Guoyi (Ph.D.) (Author) / Chattopadhyay, Aditi (Thesis advisor) / Mignolet, Marc (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Yekani Fard, Masoud (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2019
Description
This investigation focuses on the development of uncertainty modeling methods applicable to both the structural and thermal models of heated structures as part of an effort to enable the design under uncertainty of hypersonic vehicles. The maximum entropy-based nonparametric stochastic modeling approach is used within the context of coupled structural-thermal Reduced Order Models (ROMs). Not only does this strategy allow for a computationally efficient generation of samples of the structural and thermal responses but the maximum entropy approach allows to introduce both aleatoric and some epistemic uncertainty into the system.
While the nonparametric approach has a long history of applications to structural models, the present investigation was the first one to consider it for the heat conduction problem. In this process, it was recognized that the nonparametric approach had to be modified to maintain the localization of the temperature near the heat source, which was successfully achieved.
The introduction of uncertainty in coupled structural-thermal ROMs of heated structures was addressed next. It was first recognized that the structural stiffness coefficients (linear, quadratic, and cubic) and the parameters quantifying the effects of the temperature distribution on the structural response can be regrouped into a matrix that is symmetric and positive definite. The nonparametric approach was then applied to this matrix allowing the assessment of the effects of uncertainty on the resulting temperature distributions and structural response.
The third part of this document focuses on introducing uncertainty using the Maximum Entropy Method at the level of finite element by randomizing elemental matrices, for instance, elemental stiffness, mass and conductance matrices. This approach brings some epistemic uncertainty not present in the parametric approach (e.g., by randomizing the elasticity tensor) while retaining more local character than the operation in ROM level.
The last part of this document focuses on the development of “reduced ROMs” (RROMs) which are reduced order models with small bases constructed in a data-driven process from a “full” ROM with a much larger basis. The development of the RROM methodology is motivated by the desire to optimally reduce the computational cost especially in multi-physics situations where a lack of prior understanding/knowledge of the solution typically leads to the selection of ROM bases that are excessively broad to ensure the necessary accuracy in representing the response. It is additionally emphasized that the ROM reduction process can be carried out adaptively, i.e., differently over different ranges of loading conditions.
While the nonparametric approach has a long history of applications to structural models, the present investigation was the first one to consider it for the heat conduction problem. In this process, it was recognized that the nonparametric approach had to be modified to maintain the localization of the temperature near the heat source, which was successfully achieved.
The introduction of uncertainty in coupled structural-thermal ROMs of heated structures was addressed next. It was first recognized that the structural stiffness coefficients (linear, quadratic, and cubic) and the parameters quantifying the effects of the temperature distribution on the structural response can be regrouped into a matrix that is symmetric and positive definite. The nonparametric approach was then applied to this matrix allowing the assessment of the effects of uncertainty on the resulting temperature distributions and structural response.
The third part of this document focuses on introducing uncertainty using the Maximum Entropy Method at the level of finite element by randomizing elemental matrices, for instance, elemental stiffness, mass and conductance matrices. This approach brings some epistemic uncertainty not present in the parametric approach (e.g., by randomizing the elasticity tensor) while retaining more local character than the operation in ROM level.
The last part of this document focuses on the development of “reduced ROMs” (RROMs) which are reduced order models with small bases constructed in a data-driven process from a “full” ROM with a much larger basis. The development of the RROM methodology is motivated by the desire to optimally reduce the computational cost especially in multi-physics situations where a lack of prior understanding/knowledge of the solution typically leads to the selection of ROM bases that are excessively broad to ensure the necessary accuracy in representing the response. It is additionally emphasized that the ROM reduction process can be carried out adaptively, i.e., differently over different ranges of loading conditions.
ContributorsSong, Pengchao (Author) / Mignolet, Marc P (Thesis advisor) / Smarslok, Benjamin (Committee member) / Chattopadhyay, Aditi (Committee member) / Liu, Yongming (Committee member) / Jiang, Hanqing (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2019
Description
Aging-related damage and failure in structures, such as fatigue cracking, corrosion, and delamination, are critical for structural integrity. Most engineering structures have embedded defects such as voids, cracks, inclusions from manufacturing. The properties and locations of embedded defects are generally unknown and hard to detect in complex engineering structures. Therefore, early detection of damage is beneficial for prognosis and risk management of aging infrastructure system.
Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
ContributorsChang, Qinan (Author) / Liu, Yongming (Thesis advisor) / Mignolet, Marc (Committee member) / Chattopadhyay, Aditi (Committee member) / Yan, Hao (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2019
Description
The goal of this research is to couple a physics-based model with adaptive algorithms to develop a more accurate and robust technique for structural health monitoring (SHM) in composite structures. The purpose of SHM is to localize and detect damage in structures, which has broad applications to improvements in aerospace technology. This technique employs PZT transducers to actuate and collect guided Lamb wave signals. Matching pursuit decomposition (MPD) is used to decompose the signal into a cross-term free time-frequency relation. This decoupling of time and frequency facilitates the calculation of a signal's time-of-flight along a path between an actuator and sensor. Using the time-of-flights, comparisons can be made between similar composite structures to find damaged regions by examining differences in the time of flight for each path between PZTs, with respect to direction. Relatively large differences in time-of-flight indicate the presence of new or more significant damage, which can be verified using a physics-based approach. Wave propagation modeling is used to implement a physics based approach to this method, which is coupled with adaptive algorithms that take into account currently existing damage to a composite structure. Previous SHM techniques for composite structures rely on the assumption that the composite is initially free of all damage on both a macro and micro-scale, which is never the case due to the inherent introduction of material defects in its fabrication. This method provides a novel technique for investigating the presence and nature of damage in composite structures. Further investigation into the technique can be done by testing structures with different sizes of damage and investigating the effects of different operating temperatures on this SHM system.
ContributorsBarnes, Zachary Stephen (Author) / Chattopadhyay, Aditi (Thesis director) / Neerukatti, Rajesh Kumar (Committee member) / Barrett, The Honors College (Contributor) / Department of English (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
The traditional understanding of robotics includes mechanisms of rigid structures, which can manipulate surrounding objects, taking advantage of mechanical actuators such as motors and servomechanisms. Although these methods provide the underlying fundamental concepts behind much of modern technological infrastructure, in fields such as manufacturing, automation, and biomedical application, the robotic structures formed by rigid axels on mechanical actuators lack the delicate differential sensors and actuators associated with known biological systems. The rigid structures of traditional robotics also inhibit the use of simple mechanisms in congested and/or fragile environments. By observing a variety of biological systems, it is shown that nature models its structures over millions of years of evolution into a combination of soft structures and rigid skeletal interior supports. Through technological bio-inspired designs, researchers hope to mimic some of the complex behaviors of biological mechanisms using pneumatic actuators coupled with highly compliant materials that exhibit relatively large reversible elastic strain. This paper begins the brief history of soft robotics, the various classifications of pneumatic fluid systems, the associated difficulties that arise with the unpredictable nature of fluid reactions, the methods of pneumatic actuators in use today, the current industrial applications of soft robotics, and focuses in large on the construction of a universally adaptable soft robotic gripper and material application tool. The central objective of this experiment is to compatibly pair traditional rigid robotics with the emerging technologies of sort robotic actuators. This will be done by combining a traditional rigid robotic arm with a soft robotic manipulator bladder for the purposes of object manipulation and excavation of extreme environments.
ContributorsShuster, Eden S. (Author) / Thanga, Jekan (Thesis director) / Asphaug, Erik (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Advanced aerospace materials, including fiber reinforced polymer and ceramic matrix composites, are increasingly being used in critical and demanding applications, challenging the current damage prediction, detection, and quantification methodologies. Multiscale computational models offer key advantages over traditional analysis techniques and can provide the necessary capabilities for the development of a comprehensive virtual structural health monitoring (SHM) framework. Virtual SHM has the potential to drastically improve the design and analysis of aerospace components through coupling the complementary capabilities of models able to predict the initiation and propagation of damage under a wide range of loading and environmental scenarios, simulate interrogation methods for damage detection and quantification, and assess the health of a structure. A major component of the virtual SHM framework involves having micromechanics-based multiscale composite models that can provide the elastic, inelastic, and damage behavior of composite material systems under mechanical and thermal loading conditions and in the presence of microstructural complexity and variability. Quantification of the role geometric and architectural variability in the composite microstructure plays in the local and global composite behavior is essential to the development of appropriate scale-dependent unit cells and boundary conditions for the multiscale model. Once the composite behavior is predicted and variability effects assessed, wave-based SHM simulation models serve to provide knowledge on the probability of detection and characterization accuracy of damage present in the composite. The research presented in this dissertation provides the foundation for a comprehensive SHM framework for advanced aerospace materials. The developed models enhance the prediction of damage formation as a result of ceramic matrix composite processing, improve the understanding of the effects of architectural and geometric variability in polymer matrix composites, and provide an accurate and computational efficient modeling scheme for simulating guided wave excitation, propagation, interaction with damage, and sensing in a range of materials. The methodologies presented in this research represent substantial progress toward the development of an accurate and generalized virtual SHM framework.
ContributorsBorkowski, Luke (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2015
Description
In-situ fatigue damage diagnosis and prognosis is a challenging problem for both metallic and composite materials and structures. There are various uncertainties arising from material properties, component geometries, measurement noise, feature extraction techniques, and modeling errors. It is essential to manage and incorporate these uncertainties in order to achieve accurate damage detection and remaining useful life (RUL) prediction.
The aim of this study is to develop an integrated fatigue damage diagnosis and prognosis framework for both metallic and composite materials. First, Lamb waves are used as the in-situ damage detection technique to interrogate the damaged structures. Both experimental and numerical analysis for the Lamb wave propagation within aluminum are conducted. The RUL of lap joints under variable and constant fatigue loading is predicted using the Bayesian updating by incorporating damage detection information and various sources of uncertainties. Following this, the effect of matrix cracking and delamination in composite laminates on the Lamb wave propagation is investigated and a generalized probabilistic delamination size and location detection framework using Bayesian imaging method (BIM) is proposed and validated using the composite fatigue testing data. The RUL of the open-hole specimen is predicted using the overall stiffness degradation under fatigue loading. Next, the adjoint method-based damage detection framework is proposed considering the physics of heat conduction or elastic wave propagation. Different from the classical wave propagation-based method, the received signal under pristine condition is not necessary for estimating the damage information. This method can be successfully used for arbitrary damage location and shape profiling for any materials with higher accuracy and resolution. Finally, some conclusions and future work are generated based on the current investigation.
The aim of this study is to develop an integrated fatigue damage diagnosis and prognosis framework for both metallic and composite materials. First, Lamb waves are used as the in-situ damage detection technique to interrogate the damaged structures. Both experimental and numerical analysis for the Lamb wave propagation within aluminum are conducted. The RUL of lap joints under variable and constant fatigue loading is predicted using the Bayesian updating by incorporating damage detection information and various sources of uncertainties. Following this, the effect of matrix cracking and delamination in composite laminates on the Lamb wave propagation is investigated and a generalized probabilistic delamination size and location detection framework using Bayesian imaging method (BIM) is proposed and validated using the composite fatigue testing data. The RUL of the open-hole specimen is predicted using the overall stiffness degradation under fatigue loading. Next, the adjoint method-based damage detection framework is proposed considering the physics of heat conduction or elastic wave propagation. Different from the classical wave propagation-based method, the received signal under pristine condition is not necessary for estimating the damage information. This method can be successfully used for arbitrary damage location and shape profiling for any materials with higher accuracy and resolution. Finally, some conclusions and future work are generated based on the current investigation.
ContributorsPeng, Tishun (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Tang, Pingbo (Committee member) / Arizona State University (Publisher)
Created2016
Description
All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.
The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.
The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.
ContributorsKim, Inho (Author) / Chattopadhyay, Aditi (Thesis advisor) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2016