Matching Items (56)
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- All Subjects: Mechanical Engineering
- Creators: Liu, Yongming
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Fatigue fracture is one of the most common types of mechanical failures seen in structures. Considering that fatigue failures usually initiate on surfaces, it is accepted that surface roughness has a detrimental effect on the fatigue life of components. Irregularities on the surface cause stress concentrations and form nucleation sites for cracks. As surface conditions are not always satisfactory, particularly for additively manufactured components, it is necessary to develop a reliable model for fatigue life estimation considering surface roughness effects and assure structural integrity. This research study focuses on extending a previously developed subcycle fatigue crack growth model to include the effects of surface roughness. Unlike other models that consider surface irregularities as series of cracks, the proposed model is unique in the way that it treats the peaks and valleys of surface texture as a single equivalent notch. First, an equivalent stress concentration factor for the roughness was estimated and introduced into an asymptotic interpolation method for notches. Later, a concept called equivalent initial flaw size was incorporated along with linear elastic fracture mechanics to predict the fatigue life of Ti-6Al-4V alloy with different levels of roughness under uniaxial and multiaxial loading conditions. The predicted results were validated using the available literature data. The developed model can also handle variable amplitude loading conditions, which is suggested for future work.
ContributorsKethamukkala, Kaushik (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2022
Description
Non-Destructive Testing (NDT) is a branch of scientific methods and techniques
used to evaluate the defects and irregularities in engineering materials. These methods
conduct testing without destroying or altering material’s structure and functionality. Most
of these defects are subsurface making them difficult to detect and access.
SONIC INFRARED (IR) is a relatively new and emerging vibrothermography
method under the category of NDT methods. This is a fast NDT inspection method that
uses an ultrasonic generator to pass an ultrasonic pulse through the test specimen which
results in a temperature variation in the test specimen. The temperature increase around
the area of the defect is more because of frictional heating due to the vibration of the
specimen. This temperature variation can be observed using a thermal camera.
In this research study, the temperature variation in the composite laminate during
the SONIC IR experimentation using an infrared thermal camera. These recorded data are
used to determine the location, dimension and depth of defects through SONIC IR NDT
method using existing defect detection algorithms. Probability of detection analysis is
used to determine the probability of detection under specific experimental conditions for
two different types of composite laminates. Lastly, the effect of the process parameters
such as number of pulses, pulse duration and time delay between pulses of this technique
on the detectability and probability of detection is studied in detail.
used to evaluate the defects and irregularities in engineering materials. These methods
conduct testing without destroying or altering material’s structure and functionality. Most
of these defects are subsurface making them difficult to detect and access.
SONIC INFRARED (IR) is a relatively new and emerging vibrothermography
method under the category of NDT methods. This is a fast NDT inspection method that
uses an ultrasonic generator to pass an ultrasonic pulse through the test specimen which
results in a temperature variation in the test specimen. The temperature increase around
the area of the defect is more because of frictional heating due to the vibration of the
specimen. This temperature variation can be observed using a thermal camera.
In this research study, the temperature variation in the composite laminate during
the SONIC IR experimentation using an infrared thermal camera. These recorded data are
used to determine the location, dimension and depth of defects through SONIC IR NDT
method using existing defect detection algorithms. Probability of detection analysis is
used to determine the probability of detection under specific experimental conditions for
two different types of composite laminates. Lastly, the effect of the process parameters
such as number of pulses, pulse duration and time delay between pulses of this technique
on the detectability and probability of detection is studied in detail.
ContributorsDarnal, Aryabhat (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2019
Description
This thesis intends to cover the experimental investigation of the propagation of laser-generated optoacoustic waves in structural materials and how they can be utilized for damage detection. Firstly, a system for scanning a rectangular patch on the sample is designed. This is achieved with the help of xy stages which are connected to the laser head and allow it to move on a plane. Next, a parametric study was designed to determine the optimum testing parameters of the laser. The parameters so selected were then used in a series of tests which helped in discerning how the Ultrasound Waves behave when damage is induced in the sample (in the form of addition of masses). The first test was of increasing the mases in the sample. The second test was a scan of a rectangular area of the sample with and without damage to find the effect of the added masses. Finally, the data collected in such a manner is processed with the help of the Hilbert-Huang transform to determine the time of arrival. The major benefits from this study are the fact that this is a Non-Destructive imaging technique and thus can be used as a new method for detection of defects and is fairly cheap as well.
ContributorsRavi Narayanan, Venkateshwaran (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Corrosion fatigue has been of prime concern in railways, aerospace, construction industries and so on. Even in the case of many medical equipment, corrosion fatigue is considered to be a major challenge. The fact that even high strength materials have lower resistance to corrosion fatigue makes it an interesting area for research. The analysis of propagation of fatigue crack growth under environmental interaction and the life prediction is significant to reduce the maintenance costs and assure structural integrity. Without proper investigation of the crack extension under corrosion fatigue, the scenario can lead to catastrophic disasters due to premature failure of a structure. An attempt has been made in this study to predict the corrosion fatigue crack growth with reasonable accuracy. Models that have been developed so far predict the crack propagation for constant amplitude loading (CAL). However, most of the industrial applications encounter random loading. Hence there is a need to develop models based on time scale. An existing time scale model that can predict the fatigue crack growth for constant and variable amplitude loading (VAL) in the Paris region is initially modified to extend the prediction to near threshold and unstable crack growth region. Extensive data collection was carried out to calibrate the model for corrosion fatigue crack growth (CFCG) based on the experimental data. The time scale model is improved to incorporate the effect of corrosive environments such as NaCl and dry hydrogen in the fatigue crack growth (FCG) by investigation of the trend in change of the crack growth. The time scale model gives the advantage of coupling the time phenomenon stress corrosion cracking which is suggested as a future work in this paper.
ContributorsKurian, Bianca (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Information exists in various forms and a better utilization of the available information can benefit the system awareness and response predictions. The focus of this dissertation is on the fusion of different types of information using Bayesian-Entropy method. The Maximum Entropy method in information theory introduces a unique way of handling information in the form of constraints. The Bayesian-Entropy (BE) principle is proposed to integrate the Bayes’ theorem and Maximum Entropy method to encode extra information. The posterior distribution in Bayesian-Entropy method has a Bayesian part to handle point observation data, and an Entropy part that encodes constraints, such as statistical moment information, range information and general function between variables. The proposed method is then extended to its network format as Bayesian Entropy Network (BEN), which serves as a generalized information fusion tool for diagnostics, prognostics, and surrogate modeling.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
ContributorsWang, Yuhao (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Yan, Hao (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2020
Description
Additive manufacturing (AM) has been extensively investigated in recent years to explore its application in a wide range of engineering functionalities, such as mechanical, acoustic, thermal, and electrical properties. The proposed study focuses on the data-driven approach to predict the mechanical properties of additively manufactured metals, specifically Ti-6Al-4V. Extensive data for Ti-6Al-4V using three different Powder Bed Fusion (PBF) additive manufacturing processes: Selective Laser Melting (SLM), Electron Beam Melting (EBM), and Direct Metal Laser Sintering (DMLS) are collected from the open literature. The data is used to develop models to estimate the mechanical properties of Ti-6Al-4V. For this purpose, two models are developed which relate the fabrication process parameters to the static and fatigue properties of the AM Ti-6Al-4V. To identify the behavior of the relationship between the input and output parameters, each of the models is developed on both linear multi-regression analysis and non-linear Artificial Neural Network (ANN) based on Bayesian regularization. Uncertainties associated with the performance prediction and sensitivity with respect to processing parameters are investigated. Extensive sensitivity studies are performed to identify the important factors for future optimal design. Some conclusions and future work are drawn based on the proposed study with investigated material.
ContributorsSharma, Antriksh (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2020
Description
AA 7XXX alloys are used extensively in aircraft and naval structures due to their excellent strength to weight ratio. These alloys are often exposed to harsh corrosive environments and mechanical stresses that can compromise their reliability in service. They are also coupled with fasteners that are composed of different materials such as Titanium alloys. Such dissimilar metal contact facilitates galvanic and crevice corrosion, which can further reduce their lifetimes. Despite decades of research in the area, the confluence of mechanical, microstructural, and electrochemical aspects of damage is still unclear. Traditionally, 2D and destructive methods have often been employed to study the corrosion and cracking behavior in these systems which can be severely limiting and lead to inaccurate conclusions. This dissertation is aimed at comprehensively studying the corrosion and cracking behavior of these systems using time-dependent 3D microstructural characterization, as well as correlative microscopy. The microstructural evolution of corrosion in AA 7075 was studied using a combination of potentiodynamic polarization, X-ray Computed Tomography (XCT) and Transmission X-ray Microscopy (TXM). In both experiments, a strong emphasis was placed on studying localized corrosion attack at constituent particles and intergranular corrosion. With an understanding of the alloy’s corrosion behavior, a dissimilar alloy couple comprising AA 7075 / Ti-6Al-4V was then investigated. Ex situ and in situ x-ray microtomography was used extensively to investigate the evolution of pitting corrosion and corrosion fatigue in AA 7075 plates fastened separately with Ti-6Al-4V screws and rivets. The 4D tomography combined with the extensive fractography yielded valuable information pertaining the preferred sites of pit initiation, crack initiation and growth in these complex geometries. The use of correlative microscopy-based methodologies yielded multimodal characterization results that provided a unique and seminal insight on corrosion mechanisms in these materials.
ContributorsNiverty, Sridhar (Author) / Chawla, Nikhilesh (Thesis advisor) / Liu, Yongming (Committee member) / Ankit, Kumar (Committee member) / Xiao, Xianghui (Committee member) / Arizona State University (Publisher)
Created2020
Description
Precursors of carbon fibers include rayon, pitch, and polyacrylonitrile fibers that can be heat-treated for high-strength or high-modulus carbon fibers. Among them, polyacrylonitrile has been used most frequently due to its low viscosity for easy processing and excellent performance for high-end applications. To further explore polyacrylonitrile-based fibers for better precursors, in this study, carbon nanofillers were introduced in the polymer matrix to examine their reinforcement effects and influences on carbon fiber performance. Two-dimensional graphene nanoplatelets were mainly used for the polymer reinforcement and one-dimensional carbon nanotubes were also incorporated in polyacrylonitrile as a comparison. Dry-jet wet spinning was used to fabricate the composite fibers. Hot-stage drawing and heat-treatment were used to evolve the physical microstructures and molecular morphologies of precursor and carbon fibers. As compared to traditionally used random dispersions, selective placement of nanofillers was effective in improving composite fiber properties and enhancing mechanical and functional behaviors of carbon fibers. The particular position of reinforcement fillers with polymer layers was enabled by the in-house developed spinneret used for fiber spinning. The preferential alignment of graphitic planes contributed to the enhanced mechanical and functional behaviors than those of dispersed nanoparticles in polyacrylonitrile composites. The high in-plane modulus of graphene and the induction to polyacrylonitrile molecular carbonization/graphitization were the motivation for selectively placing graphene nanoplatelets between polyacrylonitrile layers. Mechanical tests, scanning electron microscopy, thermal, and electrical properties were characterized. Applications such as volatile organic compound sensing and pressure sensing were demonstrated.
ContributorsFranklin, Rahul Joseph (Author) / Song, Kenan (Thesis advisor) / Jiao, Yang (Thesis advisor) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2020
Description
Engineering materials and structures undergo a wide variety of multiaxial fatigue loading conditions during their service life. Some of the most complex multiaxial loading scenarios include proportional/non-proportional loading, mix-mode loading, overload/underload, etc. Such loadings are often experienced in many critical applications including aircraft, rotorcraft, and wind turbines. Any accidental failure of these structures during their service life can lead to catastrophic damage to life, property, and environment. All fatigue failure begins with the nucleation of a small crack, followed by crack growth, and ultimately the occurrence of final failure; however, the mechanisms governing the crack nucleation and the crack propagation behavior depend on the nature of fatigue loading and microstructure of the material. In general, ductile materials witness multiple nucleation sites leading to its failure; however, high strength material fails from the nucleation of a single dominant crack. Crack propagation, on the other hand, is governed by various competing mechanisms, which can act either ahead of the crack tip or in the wake region of the crack. Depending upon the magnitude of load, overload/underload, mode-mixity, and microstructure, dominant governing mechanisms may include: crack tip blunting; crack deflection, branching and secondary cracking; strain hardening; residual compressive stresses; plasticity-induced closure, etc. Therefore, it is essential to investigate the mechanisms governing fatigue failure of structural components under such complex multiaxial loading conditions in order to provide a reliable estimation of useful life. The research presented in this dissertation provides the foundation for a comprehensive understanding of fatigue damage in AA 7075 subjected to a range of loading conditions. A series of fatigue tests were conducted on specially designed specimens under different forms of multiaxial loading, which was followed by fracture-surface analysis in order to identify the governing micromechanisms and correlate them with macroscopic fatigue damage behavior. An empirical model was also developed to predict the crack growth rate trend under mode II overloads in an otherwise constant amplitude biaxial loading. The model parameters were calculated using the shape and the size of the plastic zone ahead of the crack tip, and the degree of material hardening within the overload plastic zone. The data obtained from the model showed a good correlation with the experimental values for crack growth rate in the transient region.
ContributorsSingh, Abhay Kumar (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Fard, Masoud Y (Committee member) / Arizona State University (Publisher)
Created2021