Matching Items (207)
Description
With the increasing focus on developing environmentally benign electronic packages, lead-free solder alloys have received a great deal of attention. Mishandling of packages, during manufacture, assembly, or by the user may cause failure of solder joint. A fundamental understanding of the behavior of lead-free solders under mechanical shock conditions is lacking. Reliable experimental and numerical analysis of lead-free solder joints in the intermediate strain rate regime need to be investigated. This dissertation mainly focuses on exploring the mechanical shock behavior of lead-free tin-rich solder alloys via multiscale modeling and numerical simulations. First, the macroscopic stress/strain behaviors of three bulk lead-free tin-rich solders were tested over a range of strain rates from 0.001/s to 30/s. Finite element analysis was conducted to determine appropriate specimen geometry that could reach a homogeneous stress/strain field and a relatively high strain rate. A novel self-consistent true stress correction method is developed to compensate the inaccuracy caused by the triaxial stress state at the post-necking stage. Then the material property of micron-scale intermetallic was examined by micro-compression test. The accuracy of this measure is systematically validated by finite element analysis, and empirical adjustments are provided. Moreover, the interfacial property of the solder/intermetallic interface is investigated, and a continuum traction-separation law of this interface is developed from an atomistic-based cohesive element method. The macroscopic stress/strain relation and microstructural properties are combined together to form a multiscale material behavior via a stochastic approach for both solder and intermetallic. As a result, solder is modeled by porous plasticity with random voids, and intermetallic is characterized as brittle material with random vulnerable region. Thereafter, the porous plasticity fracture of the solders and the brittle fracture of the intermetallics are coupled together in one finite element model. Finally, this study yields a multiscale model to understand and predict the mechanical shock behavior of lead-free tin-rich solder joints. Different fracture patterns are observed for various strain rates and/or intermetallic thicknesses. The predictions have a good agreement with the theory and experiments.
ContributorsFei, Huiyang (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Thesis advisor) / Tasooji, Amaneh (Committee member) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
Early-age cracks in fresh concrete occur mainly due to high rate of surface evaporation and restraint offered by the contracting solid phase. Available test methods that simulate severe drying conditions, however, were not originally designed to focus on evaporation and transport characteristics of the liquid-gas phases in a hydrating cementitious microstructure. Therefore, these tests lack accurate measurement of the drying rate and data interpretation based on the principles of transport properties is limited. A vacuum-based test method capable of simulating early-age cracks in 2-D cement paste is developed which continuously monitors the weight loss and changes to the surface characteristics. 2-D crack evolution is documented using time-lapse photography. Effects of sample size, w/c ratio, initial curing and fiber content are studied. In the subsequent analysis, the cement paste phase is considered as a porous medium and moisture transport is described based on surface mass transfer and internal moisture transport characteristics. Results indicate that drying occurs in two stages: constant drying rate period (stage I), followed by a falling drying rate period (stage II). Vapor diffusion in stage I and unsaturated flow within porous medium in stage II determine the overall rate of evaporation. The mass loss results are analyzed using diffusion-based models. Results show that moisture diffusivity in stage I is higher than its value in stage II by more than one order of magnitude. The drying model is used in conjunction with a shrinkage model to predict the development of capillary pressures. Similar approach is implemented in drying restrained ring specimens to predict 1-D crack width development. An analytical approach relates diffusion, shrinkage, creep, tensile and fracture properties to interpret the experimental data. Evaporation potential is introduced based on the boundary layer concept, mass transfer, and a driving force consisting of the concentration gradient. Effect of wind velocity is reflected on Reynolds number which affects the boundary layer on sample surface. This parameter along with Schmidt and Sherwood numbers are used for prediction of mass transfer coefficient. Concentration gradient is shown to be a strong function of temperature and relative humidity and used to predict the evaporation potential. Results of modeling efforts are compared with a variety of test results reported in the literature. Diffusivity data and results of 1-D and 2-D image analyses indicate significant effects of fibers on controlling early-age cracks. Presented models are capable of predicting evaporation rates and moisture flow through hydrating cement-based materials during early-age drying and shrinkage conditions.
ContributorsBakhshi, Mehdi (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Zapata, Claudia E. (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis describes an approach to system identification based on compressive sensing and demonstrates its efficacy on a challenging classical benchmark single-input, multiple output (SIMO) mechanical system consisting of an inverted pendulum on a cart. Due to its inherent non-linearity and unstable behavior, very few techniques currently exist that are capable of identifying this system. The challenge in identification also lies in the coupled behavior of the system and in the difficulty of obtaining the full-range dynamics. The differential equations describing the system dynamics are determined from measurements of the system's input-output behavior. These equations are assumed to consist of the superposition, with unknown weights, of a small number of terms drawn from a large library of nonlinear terms. Under this assumption, compressed sensing allows the constituent library elements and their corresponding weights to be identified by decomposing a time-series signal of the system's outputs into a sparse superposition of corresponding time-series signals produced by the library components. The most popular techniques for non-linear system identification entail the use of ANN's (Artificial Neural Networks), which require a large number of measurements of the input and output data at high sampling frequencies. The method developed in this project requires very few samples and the accuracy of reconstruction is extremely high. Furthermore, this method yields the Ordinary Differential Equation (ODE) of the system explicitly. This is in contrast to some ANN approaches that produce only a trained network which might lose fidelity with change of initial conditions or if facing an input that wasn't used during its training. This technique is expected to be of value in system identification of complex dynamic systems encountered in diverse fields such as Biology, Computation, Statistics, Mechanics and Electrical Engineering.
ContributorsNaik, Manjish Arvind (Author) / Cochran, Douglas (Thesis advisor) / Kovvali, Narayan (Committee member) / Kawski, Matthias (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2011
Description
The cyber-physical systems (CPS) are emerging as the underpinning technology for major industries in the 21-th century. This dissertation is focused on two fundamental issues in cyber-physical systems: network interdependence and information dynamics. It consists of the following two main thrusts. The first thrust is targeted at understanding the impact of network interdependence. It is shown that a cyber-physical system built upon multiple interdependent networks are more vulnerable to attacks since node failures in one network may result in failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. There is thus a need to develop a new network science for modeling and quantifying cascading failures in multiple interdependent networks, and to develop network management algorithms that improve network robustness and ensure overall network reliability against cascading failures. To enhance the system robustness, a "regular" allocation strategy is proposed that yields better resistance against cascading failures compared to all possible existing strategies. Furthermore, in view of the load redistribution feature in many physical infrastructure networks, e.g., power grids, a CPS model is developed where the threshold model and the giant connected component model are used to capture the node failures in the physical infrastructure network and the cyber network, respectively. The second thrust is centered around the information dynamics in the CPS. One speculation is that the interconnections over multiple networks can facilitate information diffusion since information propagation in one network can trigger further spread in the other network. With this insight, a theoretical framework is developed to analyze information epidemic across multiple interconnecting networks. It is shown that the conjoining among networks can dramatically speed up message diffusion. Along a different avenue, many cyber-physical systems rely on wireless networks which offer platforms for information exchanges. To optimize the QoS of wireless networks, there is a need to develop a high-throughput and low-complexity scheduling algorithm to control link dynamics. To that end, distributed link scheduling algorithms are explored for multi-hop MIMO networks and two CSMA algorithms under the continuous-time model and the discrete-time model are devised, respectively.
ContributorsQian, Dajun (Author) / Zhang, Junshan (Thesis advisor) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
The main objective of this study is to develop an innovative system in the form of a sandwich panel type composite with textile reinforced skins and aerated concrete core. Existing theoretical concepts along with extensive experimental investigations were utilized to characterize the behavior of cement based systems in the presence of individual fibers and textile yarns. Part of this thesis is based on a material model developed here in Arizona State University to simulate experimental flexural response and back calculate tensile response. This concept is based on a constitutive law consisting of a tri-linear tension model with residual strength and a bilinear elastic perfectly plastic compression stress strain model. This parametric model was used to characterize Textile Reinforced Concrete (TRC) with aramid, carbon, alkali resistant glass, polypropylene TRC and hybrid systems of aramid and polypropylene. The same material model was also used to characterize long term durability issues with glass fiber reinforced concrete (GFRC). Historical data associated with effect of temperature dependency in aging of GFRC composites were used. An experimental study was conducted to understand the behavior of aerated concrete systems under high stain rate impact loading. Test setup was modeled on a free fall drop of an instrumented hammer using three point bending configuration. Two types of aerated concrete: autoclaved aerated concrete (AAC) and polymeric fiber-reinforced aerated concrete (FRAC) were tested and compared in terms of their impact behavior. The effect of impact energy on the mechanical properties was investigated for various drop heights and different specimen sizes. Both materials showed similar flexural load carrying capacity under impact, however, flexural toughness of fiber-reinforced aerated concrete was proved to be several degrees higher in magnitude than that provided by plain autoclaved aerated concrete. Effect of specimen size and drop height on the impact response of AAC and FRAC was studied and discussed. Results obtained were compared to the performance of sandwich beams with AR glass textile skins with aerated concrete core under similar impact conditions. After this extensive study it was concluded that this type of sandwich composite could be effectively used in low cost sustainable infrastructure projects.
ContributorsDey, Vikram (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Woven fabric composite materials are widely used in the construction of aircraft engine fan containment systems, mostly due to their high strength to weight ratios and ease of implementation. The development of a predictive model for fan blade containment would provide great benefit to engine manufactures in shortened development cycle time, less risk in certification and fewer dollars lost to redesign/recertification cycles. A mechanistic user-defined material model subroutine has been developed at Arizona State University (ASU) that captures the behavioral response of these fabrics, namely Kevlar® 49, under ballistic loading. Previously developed finite element models used to validate the consistency of this material model neglected the effects of the physical constraints imposed on the test setup during ballistic testing performed at NASA Glenn Research Center (NASA GRC). Part of this research was to explore the effects of these boundary conditions on the results of the numerical simulations. These effects were found to be negligible in most instances. Other material models for woven fabrics are available in the LS-DYNA finite element code. One of these models, MAT234: MAT_VISCOELASTIC_LOOSE_FABRIC (Ivanov & Tabiei, 2004) was studied and implemented in the finite element simulations of ballistic testing associated with the FAA ASU research. The results from these models are compared to results obtained from the ASU UMAT as part of this research. The results indicate an underestimation in the energy absorption characteristics of the Kevlar 49 fabric containment systems. More investigation needs to be performed in the implementation of MAT234 for Kevlar 49 fabric. Static penetrator testing of Kevlar® 49 fabric was performed at ASU in conjunction with this research. These experiments are designed to mimic the type of loading experienced during fan blade out events. The resulting experimental strains were measured using a non-contact optical strain measurement system (ARAMIS).
ContributorsFein, Jonathan (Author) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Dwindling energy resources and associated environmental costs have resulted in a serious need to design and construct energy efficient buildings. One of the strategies to develop energy efficient structural materials is through the incorporation of phase change materials (PCM) in the host matrix. This research work presents details of a finite element-based framework that is used to study the thermal performance of structural precast concrete wall elements with and without a layer of phase change material. The simulation platform developed can be implemented for a wide variety of input parameters. In this study, two different locations in the continental United States, representing different ambient temperature conditions (corresponding to hot, cold and typical days of the year) are studied. Two different types of concrete - normal weight and lightweight, different PCM types, gypsum wallboard's with varying PCM percentages and different PCM layer thicknesses are also considered with an aim of understanding the energy flow across the wall member. Effect of changing PCM location and prolonged thermal loading are also studied. The temperature of the inside face of the wall and energy flow through the inside face of the wall, which determines the indoor HVAC energy consumption are used as the defining parameters. An ad-hoc optimization scheme is also implemented where the PCM thickness is fixed but its location and properties are varied. Numerical results show that energy savings are possible with small changes in baseline values, facilitating appropriate material design for desired characteristics.
ContributorsHembade, Lavannya Babanrao (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alkali-activated aluminosilicates, commonly known as "geopolymers", are being increasingly studied as a potential replacement for Portland cement. These binders use an alkaline activator, typically alkali silicates, alkali hydroxides or a combination of both along with a silica-and-alumina rich material, such as fly ash or slag, to form a final product with properties comparable to or better than those of ordinary Portland cement. The kinetics of alkali activation is highly dependent on the chemical composition of the binder material and the activator concentration. The influence of binder composition (slag, fly ash or both), different levels of alkalinity, expressed using the ratios of Na2O-to-binders (n) and activator SiO2-to-Na2O ratios (Ms), on the early age behavior in sodium silicate solution (waterglass) activated fly ash-slag blended systems is discussed in this thesis. Optimal binder composition and the n values are selected based on the setting times. Higher activator alkalinity (n value) is required when the amount of slag in the fly ash-slag blended mixtures is reduced. Isothermal calorimetry is performed to evaluate the early age hydration process and to understand the reaction kinetics of the alkali activated systems. The differences in the calorimetric signatures between waterglass activated slag and fly ash-slag blends facilitate an understanding of the impact of the binder composition on the reaction rates. Kinetic modeling is used to quantify the differences in reaction kinetics using the Exponential as well as the Knudsen method. The influence of temperature on the reaction kinetics of activated slag and fly ash-slag blends based on the hydration parameters are discussed. Very high compressive strengths can be obtained both at early ages as well as later ages (more than 70 MPa) with waterglass activated slag mortars. Compressive strength decreases with the increase in the fly ash content. A qualitative evidence of leaching is presented through the electrical conductivity changes in the saturating solution. The impact of leaching and the strength loss is found to be generally higher for the mixtures made using a higher activator Ms and a higher n value. Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) is used to obtain information about the reaction products.
ContributorsChithiraputhiran, Sundara Raman (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniyam D (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012