Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Investigation on fatigue behavior of alloys by various approaches

Description
Fatigue is a degradation process of materials that would lead to failure when materials are subjected to cyclic loadings. During past centuries, various of approaches have been proposed and utilized to help researchers understand the underlying theories of fatigue behavior of materials, as well as design engineering structures so that…

Fatigue is a degradation process of materials that would lead to failure when materials are subjected to cyclic loadings. During past centuries, various of approaches have been proposed and utilized to help researchers understand the underlying theories of fatigue behavior of materials, as well as design engineering structures so that catastrophic disasters that arise from fatigue failure could be avoided. The stress-life approach is the most classical way that academia applies to analyze fatigue data, which correlates the fatigue lifetime with stress amplitudes during cyclic loadings. Fracture mechanics approach is another well-established way, by which people regard the cyclic stress intensity factor as the driving force during fatigue crack nucleation and propagation, and numerous models (such as the well-known Paris’ law) are developed by researchers.

The significant drawback of currently widely-used fatigue analysis approaches, nevertheless, is that they are all cycle-based, limiting researchers from digging into sub-cycle regime and acquiring real-time fatigue behavior data. The missing of such data further impedes academia from validating hypotheses that are related to real-time observations of fatigue crack nucleation and growth, thus the existence of various phenomena, such as crack closure, remains controversial.

In this thesis, both classical stress-life approach and fracture-mechanics-based approach are utilized to study the fatigue behavior of alloys. Distinctive material characterization instruments are harnessed to help collect and interpret key data during fatigue crack growth. Specifically, an investigation on the sub-cycle fatigue crack growth behavior is enabled by in-situ SEM mechanical testing, and a non-uniform growth mechanism within one loading cycle is confirmed by direct observation as well as image interpretation. Predictions based on proposed experimental procedure and observations show good match with cycle-based data from references, which indicates the credibility of proposed methodology and model, as well as their capability of being applied to a wide range of materials.
ContributorsLiu, Siying (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2018
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Data-Augmented Structure-Property Mapping for Accelerating Computational Design of Advanced Material Systems

Description
Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures…

Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.
ContributorsCang, Ruijin (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
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Lighter Concrete: An In-Depth Analysis of the Effects of Recycled Plastic Aggregate in Composite Concrete

Description

The scope of this project is a combination of material science engineering and mechanical engineering. Overall, the main goal of this project is to develop a lightweight concrete that maintains its original strength profile. Initial research has shown that a plastic-concrete composite could create a more lightweight concrete than that…

The scope of this project is a combination of material science engineering and mechanical engineering. Overall, the main goal of this project is to develop a lightweight concrete that maintains its original strength profile. Initial research has shown that a plastic-concrete composite could create a more lightweight concrete than that made using the typical gravel aggregate for concrete, while still maintaining the physical strength that concrete is known for. This will be accomplished by varying the amount of plastic in the aggregate. If successful, this project would allow concrete to be used in applications it would typically not be suitable for.<br/>After testing the strength of the concrete specimens with varying fills of plastic aggregate it was determined that the control group experienced an average peak stress of 2089 psi, the 16.67% plastic group experienced an average peak stress of 2649 psi, the 33.3% plastic group experienced an average peak stress of 1852 psi, and the 50% plastic group experienced an average stress of 924.5 psi. The average time to reach the peak stress was found to be 12 minutes and 24 seconds in the control group, 15 minutes and 34 seconds in the 16.7% plastic group, 9 minutes and 45 seconds in the 33.3% plastic group, and 10 minutes and 58 seconds in the 50% plastic group. Taking the average of the normalized weights of the cylindrical samples it was determined that the control group weighed 14.773 oz/in, the 16.7% plastic group weighed 15 oz/in, the 33.3% plastic group weighed 14.573 oz/in, and the 50% plastic group weighed 12.959 oz/in. Based on these results it can be concluded that a small addition of plastic aggregate can be beneficial in creating a lighter, stronger concrete. The results show that a 16.7% fill ratio of plastic to rock aggregate can increase the failure time and the peak strength of a composite concrete. Overall, the experiment was successful in analyzing the effects of recycled plastic aggregate in composite concrete. <br/>Some possible future studies related to this subject material are adding aluminum to the concrete, having better molds, looking for the right consistency in each mixture, mixing for each mold individually, and performing other tests on the samples.
ContributorsClegg, Lauren Taylor (Co-author) / Benning, Taylor (Co-author) / Nian, Qiong (Thesis director) / Jiao, Yang (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Lighter Concrete: An In-Depth Analysis of the Effects of Recycled Plastic Aggregate in Composite Concrete

Description

The scope of this project is a combination of material science engineering and<br/>mechanical engineering. Overall, the main goal of this project is to develop a lightweight<br/>concrete that maintains its original strength profile. Initial research has shown that a<br/>plastic-concrete composite could create a more lightweight concrete than that made using the<br/>typical…

The scope of this project is a combination of material science engineering and<br/>mechanical engineering. Overall, the main goal of this project is to develop a lightweight<br/>concrete that maintains its original strength profile. Initial research has shown that a<br/>plastic-concrete composite could create a more lightweight concrete than that made using the<br/>typical gravel aggregate for concrete, while still maintaining the physical strength that concrete is<br/>known for. This will be accomplished by varying the amount of plastic in the aggregate. If<br/>successful, this project would allow concrete to be used in applications it would typically not be<br/>suitable for.<br/>After testing the strength of the concrete specimens with varying fills of plastic aggregate<br/>it was determined that the control group experienced an average peak stress of 2089 psi, the<br/>16.67% plastic group experienced an average peak stress of 2649 psi, the 33.3% plastic group<br/>experienced an average peak stress of 1852 psi, and the 50% plastic group experienced an<br/>average stress of 924.5 psi. The average time to reach the peak stress was found to be 12 minutes<br/>and 24 seconds in the control group, 15 minutes and 34 seconds in the 16.7% plastic group, 9<br/>minutes and 45 seconds in the 33.3% plastic group, and 10 minutes and 58 seconds in the 50%<br/>plastic group. Taking the average of the normalized weights of the cylindrical samples it was<br/>determined that the control group weighed 14.773 oz/in, the 16.7% plastic group weighed 15<br/>oz/in, the 33.3% plastic group weighed 14.573 oz/in, and the 50% plastic group weighed 12.959<br/>oz/in. Based on these results it can be concluded that a small addition of plastic aggregate can be<br/>beneficial in creating a lighter, stronger concrete. The results show that a 16.7% fill ratio of<br/>plastic to rock aggregate can increase the failure time and the peak strength of a composite<br/>concrete. Overall, the experiment was successful in analyzing the effects of recycled plastic<br/>aggregate in composite concrete.<br/>Some possible future studies related to this subject material are adding aluminum to the<br/>concrete, having better molds, looking for the right consistency in each mixture, mixing for each<br/>mold individually, and performing other tests on the samples.
ContributorsBenning, Taylor Ann (Co-author) / Clegg, Lauren (Co-author) / Nian, Qiong (Thesis director) / Jiao, Yang (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Materials Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Experimental Investigation and A Novel Packing Hollow Dodecahedron Model to Understand the Thermal and Mechanical Properties of Elastic Cellular Architectures

Description
Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst…

Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst devices, magnetic shielding, etc. For the engineering of the cellular foam architectures, closed-form models that can be used to predict the mechanical and thermal properties of foams are highly desired especially for the recently developed ultralight weight shellular architectures. Herein, for the first time, a novel packing three-dimensional (3D) hollow pentagonal dodecahedron (HPD) model is proposed to simulate the cellular architecture with hollow struts. An electrochemical deposition process is utilized to manufacture the metallic hollow foam architecture. Mechanical and thermal testing of the as-manufactured foams are carried out to compare with the HPD model. Timoshenko beam theory is utilized to verify and explain the derived power coefficient relation. Our HPD model is proved to accurately capture both the topology and the physical properties of hollow stochastic foam. Understanding how the novel HPD model packing helps break the conventional impression that 3D pentagonal topology cannot fulfill the space as a representative volume element. Moreover, the developed HPD model can predict the mechanical and thermal properties of the manufactured hollow metallic foams and elucidating of how the inevitable manufacturing defects affect the physical properties of the hollow metallic foams. Despite of the macro-scale stochastic foam architecture, nano gradient gyroid lattices are studied using Molecular Dynamics (MD) simulation. The simulation result reveals that, unlike homogeneous architecture, gradient gyroid not only shows novel layer-by-layer deformation behavior, but also processes significantly better energy absorption ability. The deformation behavior and energy absorption are predictable and designable, which demonstrate its highly programmable potential.
ContributorsDai, Rui (Author) / Nian, Qiong (Thesis advisor) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Liu, Yongming (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2021
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Time-based Subcycle Fatigue Life Prediction Model Considering Surface Roughness

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
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Data-driven Approach to Predict the Static and Fatigue Properties of Additively Manufactured Ti-6Al-4V

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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Energy-Based Fatigue Life Prediction Under Random Uniaxial and Multiaxial Loadings

Description
Two fatigue life prediction methods using the energy-based approach have been proposed. A number of approaches have been developed in the past five decades. This study reviews some common models and discusses the model that is most suitable for each different condition, no matter whether the model is designed…

Two fatigue life prediction methods using the energy-based approach have been proposed. A number of approaches have been developed in the past five decades. This study reviews some common models and discusses the model that is most suitable for each different condition, no matter whether the model is designed to solve uniaxial, multiaxial, or biaxial loading paths in fatigue prediction. In addition, different loading cases such as various loading and constant loading are also discussed. These models are suitable for one or two conditions in fatigue prediction. While most of the existing models can only solve single cases, the proposed new energy-based approach not only can deal with different loading paths but is applicable for various loading cases. The first energy-based model using the linear cumulative rule is developed to calculate random loading cases. The method is developed by combining Miner’s rule and the rainflow-counting algorithm. For the second energy-based method, I propose an alternative method and develop an approach to avert the rainflow-counting algorithm. Specifically, I propose to use an energy-based model by directly using the time integration concept. In this study, first, the equivalent energy concept that can transform three-dimensional loading into an equivalent loading will be discussed. Second, the new damage propagation method modified by fatigue crack growth will be introduced to deal with cycle-based fatigue prediction. Third, the time-based concept will be implemented to determine fatigue damage under every cycle in the random loading case. The formulation will also be explained in detail. Through this new model, the fatigue life can be calculated properly in different loading cases. In addition, the proposed model is verified with experimental datasets from several published studies. The data include both uniaxial and multiaxial loading paths under constant loading and random loading cases. Finally, the discussion and conclusion based on the results, are included. Additional loading cases such as the spectrum including both elastic and plastic regions will be explored in future research.
ContributorsTien, Shih-Chuan (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021

Atomic-Scale Simulations of Si-Ge-Sn Alloys Using Deep-Learning-Based Interatomic Potentials

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