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Description
An integrated experimental and numerical investigation for laser-generated optoacoustic wave propagation in structural materials is performed. First, a multi-physics simulation model is proposed to simulate the pulsed laser as a point heat source which hits the surface of an aluminum sheet. The pulsed laser source can generate a localized heating

An integrated experimental and numerical investigation for laser-generated optoacoustic wave propagation in structural materials is performed. First, a multi-physics simulation model is proposed to simulate the pulsed laser as a point heat source which hits the surface of an aluminum sheet. The pulsed laser source can generate a localized heating on the surface of the plate and induce an in-plane stress wave. ANSYS – a finite element analysis software – is used to build the 3D model and a coupled thermal-mechanical simulation is performed in which the heat flux is determined by an empirical laser-heat conversion relationship. The displacement and stress field-histories are obtained to get the time of arrival and wave propagation speed of the stress wave. The effect of an added point mass is investigated in detail to observe the local material perturbation and remote wave signals. Following this, the experimental investigation of optoacoustic wave is also performed. A new experimental setup and control is developed and assembled in-house. Various laser firing parameters are investigated experimentally and the optimal combination is used for the experimental testing. Matrix design for different testing conditions is also proposed to include the effect of wave path, sampling procedure, and local point mass on the optoacoustic wave propagation. The developed numerical simulation results are validated with experimental observations. It is shown that the proposed experimental setup can offer a potential fast scanning method for damage detection (local property change) for plate-like structural component.
ContributorsLiu, Chen (Author) / Liu, Yongming (Thesis advisor) / Wang, Liping (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Dissimilar metal joints such as aluminum-steel joints are extensively used in automobile, naval and aerospace applications and these are subjected to corrosive environmental and mechanical loading resulting in eventual failure of the structural joints. In the case of aluminum alloys under aggressive environment, the damage accumulation is predominantly due to

Dissimilar metal joints such as aluminum-steel joints are extensively used in automobile, naval and aerospace applications and these are subjected to corrosive environmental and mechanical loading resulting in eventual failure of the structural joints. In the case of aluminum alloys under aggressive environment, the damage accumulation is predominantly due to corrosion and is accelerated in presence of other metals. During recent years several approaches have been employed to develop models to assess the metal removal rate in the case of galvanic corrosion. Some of these models are based on empirical methods such as regression analysis while others are based on quantification of the ongoing electrochemical processes. Here, a numerical model for solving the Nernst- Planck equation, which captures the electrochemical process, is implemented to predict the galvanic current distribution and, hence, the corrosion rate of a galvanic couple. An experimentally validated numerical model for an AE44 (Magnesium alloy) and mild steel galvanic couple, available in the literature, is extended to simulate the mechano- electrochemical process in order to study the effect of mechanical loading on the galvanic current density distribution and corrosion rate in AE44-mild steel galvanic couple through a multiphysics field coupling technique in COMSOL Multiphysics®. The model is capable of tracking moving boundariesy of the corroding constituent of the couple by employing Arbitrary Langrangian Eulerian (ALE) method.Results show that, when an anode is under a purely elastic deformation, there is no apparent effect of mechanical loading on the electrochemical galvanic process. However, when the applied tensile load is sufficient to cause a plastic deformation, the local galvanic corrosion activity at the vicinity of the interface is increased remarkably. The effect of other factors, such as electrode area ratios, electrical conductivity of the electrolyte and depth of the electrolyte, are studied. It is observed that the conductivity of the electrolyte significantly influences the surface profile of the anode, especially near the junction. Although variations in electrolyte depth for a given galvanic couple noticeably affect the overall corrosion, the change in the localized corrosion rate at the interface is minimal. Finally, we use the model to predict the current density distribution, rate of corrosion and depth profile of aluminum alloy 7075-stainless steel 316 galvanic joints, which are extensively used in maritime structures.
ContributorsMuthegowda, Nitin Chandra (Author) / Solanki, Kiran N (Thesis advisor) / Rykaczewski, Konrad (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2015
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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

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
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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,

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
Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for

Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for a relatively small number of atoms. This thesis aims to run conventionalmolecular dynamic simulations for a particular supercell and then employ a machinelearning based approach and compare the two in hopes of developing a method togreatly reduce computational costs as well as increase the scale and time frame ofthese systems. Conventional simulations were run using interatomic potentials basedon density function theory-basedab initiocalculations. Then deep learning neuralnetwork based interatomic potentials were used run similar simulations to comparethe two approaches.
ContributorsDabir, Anirudh (Author) / Zhuang, Houlong (Thesis advisor) / Nian, Qiong (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021
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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

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