Hot deformation behavior of Ni-Fe-Ga-based ferromagnetic shape memory alloy - A study using processing map

Ni-Fe-Ga-based alloys form a new class of ferromagnetic shape memory alloys (FSMAs) that show considerable formability because of the presence of a disordered fcc gamma-phase. The current study explores the deformation processing of this alloy using an off-stoichiometric Ni55Fe59Ga26 alloy that contains the ductile gamma-phase. The hot deformation behavior of this alloy has been characterized on the basis of its flow stress variation obtained by isothermal constant true strain rate compression tests in the 1123-1323 K temperature range and strain rate range of 10(-3)-10 s(-1) and using a combination of constitutive modeling and processing map. The dynamic recrystallization (DRX) regime for thermomechanical processing has been identified for this Heusler alloy on the basis of the processing maps and the deformed microstructures. This alloy also shows evidence of dynamic strain-aging (DSA) effect which has not been reported so far for any Heusler FSMAs. Similar effect is also noticed in a Ni-Mn-Ga-based Heusler alloy which is devoid of any gamma-phase. (C) 2014 Elsevier Ltd. All rights reserved.

Effects of Interface Treatment on the Fatigue Behavior of Shape Memory Alloy Reinforced Polymer Composites

Interfacial properties of Shape Memory Alloy (SMA) reinforced polymer matrix composites can be enhanced by improving the interfacial bonding. This paper focuses on studying the interfacial stresses developed in the SMA-epoxy interface due to various laser shot penning conditions. Fiber-pull test-setup is designed to understand the role of mechanical bias stress cycling and thermal actuation cycling. Phase transformation is tracked over mechanical and thermal fatigue cycles. A micromechanics based model developed earlier based on shear lag in SMA and energy based consistent homogenization is extended here to incorporate the stress-temperature phase diagram parameters for modeling fatigue.

Modeling Ultrasonic NDE and Guided Wave based Structural Health Monitoring

Structural Health Monitoring (SHM) systems require integration of non-destructive technologies into structural design and operational processes. Modeling and simulation of complex NDE inspection processes are important aspects in the development and deployment of SHM technologies. Ray tracing techniques are vital simulation tools to visualize the wave path inside a material. These techniques also help in optimizing the location of transducers and their orientation with respect to the zone of interrogation. It helps in increasing the chances of detection and identification of a flaw in that zone. While current state-of-the-art techniques such as ray tracing based on geometric principle help in such visualization, other information such as signal losses due to spherical or cylindrical shape of wave front are rarely taken into consideration. The problem becomes a little more complicated in the case of dispersive guided wave propagation and near-field defect scattering. We review the existing models and tools to perform ultrasonic NDE simulation in structural components. As an initial step, we develop a ray-tracing approach, where phase and spectral information are preserved. This enables one to study wave scattering beyond simple time of flight calculation of rays. Challenges in terms of theory and modelling of defects of various kinds are discussed. Various additional considerations such as signal decay and physics of scattering are reviewed and challenges involved in realistic computational implementation are discussed. Potential application of this approach to SHM system design is highlighted and by applying this to complex structural components such as airframe structures, SHM is demonstrated to provide additional value in terms of lighter weight and/or longevity enhancement resulting from an extension of the damage tolerance design principle not compromising safety and reliability.

Modeling Nonlinear Peeling of Ductile Thin Films-Critical Assessment of Analytical Bending Models Using FE Simulations

Three analytical double-parameter criteria based on a bending model and a two-dimensional finite element analysis model are presented for the modeling of ductile thin film undergoing a nonlinear peeling process. The bending model is based on different governing parameters: (1) the interfacial fracture toughness and the separation strength, (2) the interfacial fracture toughness and the crack tip slope angle, and (3) the interfacial fracture toughness and the critical Mises effective strain of the delaminated thin film at the crack tip. Thin film nonlinear peeling under steady-state condition is solved with the different governing parameters. In addition, the peeling test problem is simulated by using the elastic-plastic finite element analysis model. A critical assessment of the three analytical bending models is made by comparison of the bending model solutions with the finite element analysis model solutions. Furthermore, through analyses and comparisons for solutions based on both the bending model and the finite element analysis model, some connections between the bending model and the finite element analysis model are developed. Moreover, in the present research, the effect of different selections for cohesive zone shape on the ductile film peeling solutions is discussed.

The Head-On Colliding Process Of Binary Liquid Droplets At Low Velocity: High-Speed Photography Experiments And Modeling

The experimental and theoretical studies are reported in this paper for the head-on collisions of a liquid droplet with another of the same fluid resting on a solid substrate. The droplet on the hydrophobic polydimethylsiloxane (PDMS) substrate remains in a shape of an approximately spherical segment and is isometric to an incoming droplet. The colliding process of the binary droplets was recorded with high-speed photography. Head-on collisions saw four different types of response in our experiments: complete rebound, coalescence, partial rebound With conglutination, and coalescence accompanied by conglutination. For a complete rebound, both droplets exhibited remarkable elasticity and the contact time of the two colliding droplets was found to be in the range of 10-20 ms. With both droplets approximately considered as elastic bodies, Hertz contact theory was introduced to estimate the contact time for the complete rebound case. The estimated result Was found to be on the same order of magnitude as the experimental data, which indicates that the present model is reasonable. (C) 2008 Elsevier Inc. All rights reserved.

Micromechanics Modeling Of Strength For Nanocrystalline Copper

We present a model in this paper for predicting the inverse Hall-Petch phenomenon in nanocrystalline (NC) materials which are assumed to consist of two phases: grain phase of spherical or spheroidal shapes and grain boundary phase. The deformation of the grain phase has an elasto-viscoplastic behavior, which includes dislocation glide mechanism, Coble creep and Nabarro-Herring creep. However the deformation of grain boundary phase is assumed to be the mechanism of grain boundary diffusion. A Hill self-consistent method is used to describe the behavior of nanocrystalline pure copper subjected to uniaxial tension. Finally, the effects of grain size and its distribution, grain shape and strain rate on the yield strength and stress-strain curve of the pure copper are investigated. The obtained results are compared with relevant experimental data in the literature.

Discrete Modeling of Granular Media: A NURBS-based Approach

This dissertation is concerned with the development of a new discrete element method (DEM) based on Non-Uniform Rational Basis Splines (NURBS). With NURBS, the new DEM is able to capture sphericity and angularity, the two particle morphological measures used in characterizing real grain geometries. By taking advantage of the parametric nature of NURBS, the Lipschitzian dividing rectangle (DIRECT) global optimization procedure is employed as a solution procedure to the closest-point projection problem, which enables the contact treatment of non-convex particles. A contact dynamics (CD) approach to the NURBS-based discrete method is also formulated. By combining particle shape flexibility, properties of implicit time-integration, and non-penetrating constraints, we target applications in which the classical DEM either performs poorly or simply fails, i.e., in granular systems composed of rigid or highly stiff angular particles and subjected to quasistatic or dynamic flow conditions. The CD implementation is made simple by adopting a variational framework, which enables the resulting discrete problem to be readily solved using off-the-shelf mathematical programming solvers. The capabilities of the NURBS-based DEM are demonstrated through 2D numerical examples that highlight the effects of particle morphology on the macroscopic response of granular assemblies under quasistatic and dynamic flow conditions, and a 3D characterization of material response in the shear band of a real triaxial specimen.

A new method of modeling cladding band structure of air-guiding photonic crystal fibers

Cladding band structure of air-guiding photonic crystal fibers with high air-filling fraction is calculated in terms of fiber shape variation. The fundamental photonic band gap dependence on structure parameters, air-filling fraction and spacing, is also investigated. The numerical results show that the band gap edges shift toward longer wavelength as the air-filling fraction is increased, whereas the relative band gap width increases linearly. For a fixed air-filling fraction, the band gap edges with respect to spacing keep constant. With this method, the simulation results agree well with the reported data. © 2007 Elsevier B.V. All rights reserved.

Optical constants of cubic GaN/GaAs(001): Experiment and modeling

The optical constants epsilon(E)=epsilon(1)(E)+iepsilon(2)(E) of unintentionally doped cubic GaN grown on GaAs(001) have been measured at 300 K using spectral ellipsometry in the range of 1.5-5.0 eV. The epsilon(E) spectra display a structure, associated with the critical point at E-0 (direct gap) and some contribution mainly coming from the E-1 critical point. The experimental data over the entire measured spectral range (after oxide removal) has been fit using the Holden-Munoz model dielectric function [M. Munoz et al., J. Appl. Phys. 92, 5878 (2002)]. This model is based on the electronic energy-band structure near critical points plus excitonic and band-to-band Coulomb-enhancement effects at E-0, E-0 + Delta(0) and the E-1, E-1 + Delta(1), doublet. In addition to evaluating the energy of the E-0 critical point, the binding energy (R-1) of the two-dimensional exciton related to the E-1 critical point was estimated using the effective mass/k.p theory. The line, shape of the imaginary part of the cubic-GaN dielectric function shows excitonic effects at room temperature not withstanding that the exciton was not resolved. (C) 2003 American Institute of Physics.

A study of stone fragmentation in shock wave lithotripsy by customizing the acoustic field and waveform shape

Shock wave lithotripsy is the preferred treatment modality for kidney stones in the United States. Despite clinical use for over twenty-five years, the mechanisms of stone fragmentation are still under debate. A piezoelectric array was employed to examine the effect of waveform shape and pressure distribution on stone fragmentation in lithotripsy. The array consisted of 170 elements placed on the inner surface of a 15 cm-radius spherical cap. Each element was driven independently using a 170 individual pulsers, each capable of generating 1.2 kV. The acoustic field was characterized using a fiber optic probe hydrophone with a bandwidth of 30 MHz and a spatial resolution of 100 μm. When all elements were driven simultaneously, the focal waveform was a shock wave with peak pressures p+ =65±3MPa and p−=−16±2MPa and the −6 dB focal region was 13 mm long and 2 mm wide. The delay for each element was the only control parameter for customizing the acoustic field and waveform shape, which was done with the aim of investigating the hypothesized mechanisms of stone fragmentation such as spallation, shear, squeezing, and cavitation. The acoustic field customization was achieved by employing the angular spectrum approach for modeling the forward wave propagation and regression of least square errors to determine the optimal set of delays. Results from the acoustic field customization routine and its implications on stone fragmentation will be discussed.

Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling

A combined 2D, 3D approach is presented that allows for robust tracking of moving bodies in a given environment as observed via a single, uncalibrated video camera. Tracking is robust even in the presence of occlusions. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that combines low-level (image processing) and mid-level (recursive trajectory estimation) information obtained during the tracking process. The resulting system can segment and maintain the tracking of moving objects before, during, and after occlusion. At each frame, the system also extracts a stabilized coordinate frame of the moving objects. This stabilized frame is used to resize and resample the moving blob so that it can be used as input to motion recognition modules. The approach enables robust tracking without constraining the system to know the shape of the objects being tracked beforehand; although, some assumptions are made about the characteristics of the shape of the objects, and how they evolve with time. Experiments in tracking moving people are described.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

A Neural Model of 3D Shape-From-Texture: Multiple-Scale Filtering, Boundary Grouping, and Surface Filling-In

A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially discrete 2D texture elements are transformed into a spatially smooth surface representation of 3D shape. (2) Changes in the statistical properties of texture elements across space induce the perceived 3D shape of this surface representation. This is achieved in the model through multiple-scale filtering of a 2D image, followed by a cooperative-competitive grouping network that coherently binds texture elements into boundary webs at the appropriate depths using a scale-to-depth map and a subsequent depth competition stage. These boundary webs then gate filling-in of surface lightness signals in order to form a smooth 3D surface percept. The model quantitatively simulates challenging psychophysical data about perception of prolate ellipsoids (Todd and Akerstrom, 1987, J. Exp. Psych., 13, 242). In particular, the model represents a high degree of 3D curvature for a certain class of images, all of whose texture elements have the same degree of optical compression, in accordance with percepts of human observers. Simulations of 3D percepts of an elliptical cylinder, a slanted plane, and a photo of a golf ball are also presented.

An integrated modeling approach to solder joint formation

The attachment of electronic components to printed circuit boards using solder material is a complex process. This paper presents a novel modeling methodology, which integrates the governing physics taking place. Multiphysics modeling technology, imbedded into the simulation tool—PHYSICA is used to simulate fluid flow, heat transfer, solidification, and stress evolution in an integrated manner. Results using this code are presented, detailing the mechanical response of two solder materials as they cool, solidify and then deform. The shape that a solder joint takes upon melting is predicted using the SURFACE EVOLVER code. Details are given on how these predictions can be used in the PHYSICA code to provide a modeling route by which the shape, solidification history, and resulting stress profiles can be predicted.