Ultrasonic Defect Mapping Using Signal Correlation for Nondestructive Evaluation (NDE)

This article presents the application of a signal correlation technique to automatically classify ultrasonic A-scan signals for defect and defect-free regions in isotropic and anisotropic materials. First, feature extraction was implemented by generating a reference A-scan signal of a defect-free area using an autocorrelation function and statistics. Then, a cross-correlation function, utilized as a feature detector, was applied to the reference signal and a signal of interest (SOI) to detect defect-free features in an SOI. The correlation result was considered as a pattern containing both defect and defect-free features. Next, the pattern was classified by measuring the similarity between features of the reference signal and an SOI based on their Euclidean distance. Each A-scan signal classification result was then plotted on a 2D map based on its position on the specimen. The present work uses multiple correlation functions and statistics to classify defect signals rather than relying on an inspector’s prior knowledge to interpret C-scan data, and has particular value in automated ultrasonic signal classification and characterization.     

Reproducing consistent wireless protocol performance across environments

Full scale experimentation with wireless networks in deployment environments is difficult. Therefore a common validation technique is to test a prototype network in a convenient environment prior to deployment. In this paper, we consider the problem of obtaining comparable protocol performance when the test and deployment environments differ in RF propagation environment and/or inter-node spacing. To achieve comparable protocol behavior in the two settings, we propose the concept of “link usage spectrum”. Based on the hypothesis that the link usage spectrum is a gross predictor for network performance, we show how to replicate in the test setting the link usage spectrum of the protocol that is expected in the deployment setting. We show our technique for achieving comparable protocol behavior via experiments and simulations in multiple indoor and outdoor propagation environments. The link usage spectrum is protocol specific; we illustrate for a family of protocols how the link usage spectrum is calculated analytically, from the protocol metric for choosing forwarding links in the network, and how power scaling can be used to match the link usage spectrum across networks.     

Tensile Behavior of Ferrite-Carbide and Ferrite-Martensite Steels with Different Ferrite Grain Structures

Ferrite-carbide and ferrite-martensite dual-phase microstructures have been produced in a low-carbon steel with different ferrite grain structures such as, uniform distribution of coarse- and very fine-ferrite grains, and bimodal distribution of ferrite grain sizes comprising of coarse grains (~12 μm) and very fine grains (<2 μm). Very fine-grained dual-phase structure offered the best combination of tensile-strength and ductility among all the samples. The above microstructures have been compared in terms of their strain-hardening rate and the mechanism of plastic deformation.     

Investigating surface roughness of parts produced by SLS process

Selective laser sintering (SLS) has been recognized as one of the best rapid prototyping (RP) technique for producing solid models, directly from computer-aided design data by fussing together different layers with the help of laser light. Further, RP has traditionally been used for producing a solid model for visualization purpose and assessing kinematic functionality. So, the model is required to have superior mechanical integrity and surface quality for handling and model testing. This study investigates surface roughness (SR) of parts produced by SLS process. The empirical models have been purposed to predict the feasibility of different process parameters viz., laser power, scan spacing, bed temperature, hatch length, and scan count on SR. Further, these parameters have been optimized using face-centered central composite design with response surface methodology. The optimized parameters have been verified by conducting confirmation experiments.     

Laser-Tuned Surface Wettability Modification and Incorporation of Aluminum Nitride (AlN) Ceramics in Thermal Management Devices

Aluminum nitride (AlN), a versatile ceramic with high thermal conductivity, high electrical resistivity, and a coefficient of thermal expansion compatible with silicon, is well-suited for direct-to-chip cooling applications of electronics, implementation in wide bandgap semiconductors, and for high-temperature heat exchangers. Despite multiple advantages, AlN's implementation in liquid-cooling applications is often hindered by surface-degrading effects of working-fluid-induced hydrolysis. Herein, a scalable -but highly tunable- wettability engineering approach is introduced, that allows effective implementation of bulk AlN substrates in enhanced two-phase cooling of electronics. The approach prevents hydrolysis of AlN by aqueous media and establishes control over surface roughness, all the while maintaining bulk integrity and material properties of the underlying substrate. Demonstration of the new approach is presented in spontaneous, pumpless, surface liquid transport, a necessity if such ceramics are to play an integral role as components of sealed, phase-change, wickless thermal-management devices (e.g., vapor chambers or heat pipes) that require rapid working-fluid transport in their multi-phase interior. The novelty of this work lies in establishing a scalable methodology for utilizing and further enhancing the properties of this non-oxide ceramic material for phase-change heat-transfer hermetic devices, thereby paving the way toward the implementation of this intriguing material in next-generation heat spreaders.     

Two-dimensional mullite nanostructure: Synthesis and reinforcement effect on polypropylene/maleic anhydride graft ethylene vinyl acetate matrix

In the present work, we studied the reinforcement effect of mullite nanosheets (MNs) on the properties of polypropylene (PP)/maleic anhydride graft ethylene-vinyl acetate (EVA-g-MA) blend matrix. MNs have been prepared in the presence of polyvinylpyrrolidone (PVP) as the macromolecular surfactant through the sol–gel process followed by a thermal annealing method. The sheet-like morphology of mullite was confirmed by transmission electron microscopy analysis. The surface of the MNs was modified using 3-aminopropyltriethoxysilane. One weight percent of amine-functionalized MNs (AMNs) improved the impact strength of PP/EVA-g-MA blend matrix, which is about 52%. Melt rheology analysis resulted that the nanocomposite exhibited a viscous behavior up to 2.5 wt % of AMNs and above the content ensued a viscoelastic solid-like behavior. X-ray diffraction analysis showed a complete dispersion of AMNs in the PP/EVA-g-MA blend matrix. TEM analysis confirmed that the MNs sheets are residing mostly in EVA-g-MA phase and the interface of PP/EVA-g-MA blend. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48233.     

Trust Model for IoT Using Cluster Analysis: A Centralized Approach

At lower noise levels, the majority of filter-based impulse noise removal approaches outperform each other. The purpose of this paper is to design an efficient adaptive pulse coupled neural network (APCNN) technique with improved alpha guided grey wolf optimization (IAgGWO) for the elimination of high-density impulse noise. This noise reduction technique is divided into two stages: the detection of noisy pixels and the replacement of a noisy pixel with a data pixel. The IAgGWO technique is utilised to isolate the optimal values for identifying impulse noisy pixels, and the APCNN filtering technique is used to supplant them. This technique provides more accurate and clean filtered images while preserving critical edge pixel information. To demonstrate the IAgGWO-APCNN strategy's efficacy, various degrees of impulse noise were applied to the image and tested. With PSNR of 42 percent, SSIM of 99 percentand STD of 40 percent on satellite pictures, the suggested noise removal model has proved its unshakable consistency in terms of both qualitative and quantitative assessment.

     

Convergence of iteration systems

Summary An iteration system is a set of assignment statements whose computation proceeds in steps: at each step, an arbitrary subset of the statements is executed in parallel. The set of statements thus executed may differ at each step; however, it is required that each statement is executed infinitely often along the computation. The convergence of such systems (to a fixed point) is typically verified by showing that the value of a given variant function is decreased by each step that causes a state change. Such a proof requires an exponential number of cases (in the number of assignment statements) to be considered. In this paper, we present alternative methods for verifying the convergence of iteration systems. In most of these methods, upto a linear number of cases need to be considered. Anish Arora is currently completing his Ph.D. degree at the University of Texas at Austin, and has been working in the Software Technology Program at MCC since December 1988. Anish received a B.Tech. degree in Computer Science and Engineering from the Indian Institute of Technology, New Dehli in 1986, and an M.S. degree in Computer Sciences from the University of Texas at Austin in 1988. His research interests inclde fault-tolerance, distributed computing, program correctness, and semantics of concurrency. Paul Attie is currently completing his Ph.D. degree of the University of Texas at Austin, and has been a member of technical staff in the Software Technology Program of the MCC since January 1990. Paul received a B.A. degree in Engineering Science from Oxford University in 1981, and an M.Sc. degree from the University of London in 1982. His research interests include distributed computing, temporal logic, and algebraic process theory. Michael Evangelist received his Ph.D. in 1978 from Northwestern, where he did research in formal language theory, graph theory, logic, and computational complexity theory. He taught computer science at Colgate University and, in 1982, became a member of technical staff at Bell Labs and did research in software engineering. Three years later, he joined the Software Technology Program at MCC, where he spent five years working on theoretical and practical issues in the design of distributed systems. Evangelist now heads the Software Engineering Laboratory in the Chicago research center of Andersen Consulting. Mohamed G. Gouda currently works on and teaches the stabilization of computing systems at the University of Texas at Austin. He designs cute communication protocols as a hobby, and proves them correct for fun.