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- All Subjects: Mechanical Engineering
- Creators: Jiang, Hanqing
- Member of: ASU Electronic Theses and Dissertations
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
Polymer fibers have broad applications in wearable electronics, bulletproof vests, batteries, fuel cells, filters, electrodes, conductive wires, and biomedical materials. Polymer fibers display light density and flexibility but are mostly weak and compliant. The ceramic, metallic, and carbon nanoparticles have been frequently included in polymers for fabricating continuous, durable, and functional composite fibers. Nanoparticles display large specific areas, low defect density and can transfer their superior properties to polymer matrices. The main focus of this thesis is to design, fabricate and characterize the polymer
anocarbon composite fibers with unique microstructures and improved mechanical/thermal performance. The dispersions and morphologies of graphene nanoplatelets (GNPs), the interactions with polyvinyl alcohol (PVA) molecules and their influences on fiber properties are studied. The fibers were fabricated using a dry-jet wet spinning method with engineered spinneret design. Three different structured fibers were fabricated, namely, one-phase polymer fiber (1-phase), two-phase core-shell composite fiber (2-phase), and three-phase co-axial composite fiber (3-phase). These polymer or composite fibers were processed at three stages with drawing temperatures of 100˚C, 150˚C, and 200˚C. Different techniques including the mechanical tester, wide-angle X-Ray diffraction (WAXD), scanning electron microscope (SEM), thermogravimetric analysis (TGA), and differential scanning calorimeter (DSC) have been used to characterize the fiber microstructures and properties.
anocarbon composite fibers with unique microstructures and improved mechanical/thermal performance. The dispersions and morphologies of graphene nanoplatelets (GNPs), the interactions with polyvinyl alcohol (PVA) molecules and their influences on fiber properties are studied. The fibers were fabricated using a dry-jet wet spinning method with engineered spinneret design. Three different structured fibers were fabricated, namely, one-phase polymer fiber (1-phase), two-phase core-shell composite fiber (2-phase), and three-phase co-axial composite fiber (3-phase). These polymer or composite fibers were processed at three stages with drawing temperatures of 100˚C, 150˚C, and 200˚C. Different techniques including the mechanical tester, wide-angle X-Ray diffraction (WAXD), scanning electron microscope (SEM), thermogravimetric analysis (TGA), and differential scanning calorimeter (DSC) have been used to characterize the fiber microstructures and properties.
ContributorsVerma, Rahul (Author) / Song, Kenan (Thesis advisor) / Jiang, Hanqing (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this research work, the process optimization of silver iodide-silver meta phosphate ionic glass molding for solid state super ionic stamping was performed. Solid state super ionic stamping is a process of all solid ambient condition electrochemical nano patterning technique. In solid state super ionic stamping, anodic dissolution on a solid electrolyte –metal interface and subsequent charge-mass transport in the solid electrolyte is used for obtaining nanometer features on the metallic surface. The solid electrolyte referred to as the stamp is pre-patterned with features to be obtained on the metallic surface. This research developed the process for obtaining stamp with specific dimensions by making use of compression molding. The compression molding process was optimized by varying the five process parameters-temperature, pressure, holding time, pressing time and cooling time. The objective of the process optimization was to obtain best geometrical features for the stamp including flatness and surface roughness and by optimizing the compression molding process, stamp with minimum flatness and surface roughness was obtained. After the experimental optimization of the process was completed, statistical analysis was performed to understand the relative significance of the process parameters and the interaction of the process parameters on the flatness and surface roughness values of the molded stamp. Structural characterization was performed to obtain the variation of average domain size of ionic glass particles within the molded glass disk by varying the process parameters of holding time, pressing time and cooling time.
ContributorsPanikkar, Gautam (Author) / Hsu, Keng H (Thesis advisor) / Chan, Candace (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2015
Description
Skin electronics is one of the most promising applications of stretchable electronics. The versatility of skin electronics can only be guaranteed when it has conformal contact with human skin. While both analytical and numerical solutions for contact between serpentine interconnects and soft substrate remain unreported, the motivation of this thesis is to render a novel method to numerically study the conformability of the serpentine interconnects. This thesis explained thoroughly how to conduct finite element analysis for the conformability of skin electronics, including modeling, meshing method and step setup etc.. User-defined elements were implemented to the finite element commercial package ABAQUS for the analysis of conformability. With thorough investigation into the conformability of Fermat’s spiral, it has been found that the kirigami based pattern exhibits high conformability. Since thickness is a key factor to design skin electronics, the thesis also talked about how the change of thickness of the skin electronics impacts on the conformability.
ContributorsFan, Yiling (Author) / Jiang, Hanqing (Thesis advisor) / Hildreth, Owen (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Shock loading is a complex phenomenon that can lead to failure mechanisms such as strain localization, void nucleation and growth, and eventually spall fracture. The length scale of damage with respect to that of the surrounding microstructure has proven to be a key aspect in determining sites of failure initiation. Studying incipient stages of spall damage is of paramount importance to accurately determine initiation sites in the material microstructure where damage will nucleate and grow and to formulate continuum models that account for the variability of the damage process due to microstructural heterogeneity, which is the focus of this research. Shock loading experiments were conducted via flyer-plate impact tests for pressures of 2-6 GPa and strain rates of 105/s on copper polycrystals of varying thermomechanical processing conditions. Serial cross sectioning of recovered target disks was performed along with electron microscopy, electron backscattering diffraction (EBSD), focused ion beam (FIB) milling, and 3-D X-ray tomogrpahy (XRT) to gain 2-D and 3-D information on the spall plane and surrounding microstructure. Statistics on grain boundaries (GB) containing damage were obtained from 2-D data and GBs of misorientations 25° and 50° were found to have the highest probability to contain damage in as-received (AR), heat treated (HT), and fully recrystallized (FR) microstructures, while {111} Σ3 GBs were globally strong. The AR microstructure’s probability peak was the most pronounced indicating GB strength is the dominant factor for damage nucleation. 3-D XRT data was used to digitally render the spall planes of the AR, HT, and FR microstructures. From shape fitting the voids to ellipsoids, it was found that the AR microstructure contained greater than 55% intergranular damage, whereas the HT and FR microstructures contained predominantly transgranular and coalesced damage modes, respectively. 3-D reconstructions of large volume damage sites in shocked Cu multicrystals showed preference for damage nucleation at GBs between adjacent grains of a high Taylor factor mismatches as well as an angle between the shock direction and the GB physical normal of ~30°-45°. 3-D FIB sectioning of individual voids led to the discovery of uniform plastic zones ~25-50% the size of the void diameter and plastic deformation directions were characterized via local average misorientation maps. Incipient transgranular voids revealed from the sectioning process were present in grains of high Taylor factors along the shock direction, which is expected as materials with a low Taylor factor along the shock direction are susceptible to growth due their accomodation of plastic deformation. Fabrication of square waves using photolithography and chemical etching was developed to study the nature of plasticity at GBs away from the spall plane. Grains oriented close to <0 1 1> had half the residual amplitudes than grains oriented close to <0 0 1>.
ContributorsBrown, Andrew (Author) / Peralta, Pedro (Committee member) / Mignolet, Marc (Committee member) / Sieradzki, Karl (Committee member) / Solanki, Kiran (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2015
Description
Origami and Kirigami are two traditional art forms in the world. Origami, from
‘ori’ meaning folding, and ‘kami’ meaning paper is the art of paper folding. Kirigami, from ‘kiri’ meaning cutting, is the art of the combination of paper cutting and paper folding. In this dissertation, Origami and kirigami concepts were successively utilized in making stretchable lithium ion batteries and three-dimensional (3D) silicon structure which both provide excellent mechanical characteristics.
‘ori’ meaning folding, and ‘kami’ meaning paper is the art of paper folding. Kirigami, from ‘kiri’ meaning cutting, is the art of the combination of paper cutting and paper folding. In this dissertation, Origami and kirigami concepts were successively utilized in making stretchable lithium ion batteries and three-dimensional (3D) silicon structure which both provide excellent mechanical characteristics.
ContributorsSong, Zeming (Author) / Jiang, Hanqing (Thesis advisor) / Dai, Lenore (Committee member) / Yu, Hongbin (Committee member) / He, Ximin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Polymer matrix composites (PMCs) are attractive structural materials due to their high stiffness to low weight ratio. However, unidirectional PMCs have low shear strength and failure can occur along kink bands that develop on compression due to plastic microbuckling that carry strains large enough to induce nonlinear matrix deformation. Reviewing the literature, a large fraction of the existing work is for uniaxial compression, and the effects of stress gradients, such as those present during bending, have not been as well explored, and these effects are bound to make difference in terms of kink band nucleation and growth. Furthermore, reports on experimental measurements of strain fields leading to and developing inside these bands in the presence of stress gradients are also scarce and need to be addressed to gain a full understanding of their behavior when UDCs are used under bending and other spatially complex stress states.
In a light to bridge the aforementioned gaps, the primary focus of this work is to understand mechanisms for kink band evolution under an influence of stress-gradients induced during bending. Digital image correlation (DIC) is used to measure strains inside and around the kink bands during 3-point bending of samples with 0°/90° stacking made of Ultra-High Molecular Weight Polyethylene Fibers. Measurements indicate bands nucleate at the compression side and propagate into the sample carrying a mixture of large shear and normal strains (~33%), while also decreasing its bending stiffness. Failure was produced by a combination of plastic microbuckling and axial splitting. The microstructure of the kink bands was studied and used in a microstructurally explicit finite element model (FEM) to analyze stresses and strains at ply level in the samples during kink band evolution, using cohesive zone elements to represent the interfaces between plies. Cohesive element properties were deduced by a combination of delamination, fracture and three-point bending tests used to calibrate the FEMs. Modeling results show that the band morphology is sensitive to the shear and opening properties of the interfaces between the plies.
In a light to bridge the aforementioned gaps, the primary focus of this work is to understand mechanisms for kink band evolution under an influence of stress-gradients induced during bending. Digital image correlation (DIC) is used to measure strains inside and around the kink bands during 3-point bending of samples with 0°/90° stacking made of Ultra-High Molecular Weight Polyethylene Fibers. Measurements indicate bands nucleate at the compression side and propagate into the sample carrying a mixture of large shear and normal strains (~33%), while also decreasing its bending stiffness. Failure was produced by a combination of plastic microbuckling and axial splitting. The microstructure of the kink bands was studied and used in a microstructurally explicit finite element model (FEM) to analyze stresses and strains at ply level in the samples during kink band evolution, using cohesive zone elements to represent the interfaces between plies. Cohesive element properties were deduced by a combination of delamination, fracture and three-point bending tests used to calibrate the FEMs. Modeling results show that the band morphology is sensitive to the shear and opening properties of the interfaces between the plies.
ContributorsPatel, Jay K (Author) / Peralta, Pedro D (Thesis advisor) / Oswald, Jay (Committee member) / Jiang, Hanqing (Committee member) / Solanki, Kiran (Committee member) / Ayyar, Adarsh (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
Description
A new critical plane-energy model is proposed in this thesis for multiaxial fatigue life prediction of homogeneous and heterogeneous materials. Brief review of existing methods, especially on the critical plane-based and energy-based methods, are given first. Special focus is on one critical plane approach which has been shown to work for both brittle and ductile metals. The key idea is to automatically change the critical plane orientation with respect to different materials and stress states. One potential drawback of the developed model is that it needs an empirical calibration parameter for non-proportional multiaxial loadings since only the strain terms are used and the out-of-phase hardening cannot be considered. The energy-based model using the critical plane concept is proposed with help of the Mroz-Garud hardening rule to explicitly include the effect of non-proportional hardening under fatigue cyclic loadings. Thus, the empirical calibration for non-proportional loading is not needed since the out-of-phase hardening is naturally included in the stress calculation. The model predictions are compared with experimental data from open literature and it is shown the proposed model can work for both proportional and non-proportional loadings without the empirical calibration. Next, the model is extended for the fatigue analysis of heterogeneous materials integrating with finite element method. Fatigue crack initiation of representative volume of heterogeneous materials is analyzed using the developed critical plane-energy model and special focus is on the microstructure effect on the multiaxial fatigue life predictions. Several conclusions and future work is drawn based on the proposed study.
ContributorsWei, Haoyang (Author) / Liu, Yongming (Thesis advisor) / Jiang, Hanqing (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2016