Influence of machining parameters and new nano-coated tool on drilling performance of CFRP/Aluminium sandwich

Drilling and fastening of hybrid materials in one-shot operation reduces cycle time of assembly of aerospace structures. One of the most common problems encountered in automatic drilling and riveting of multimaterial is that the continuous chips curl up on the body of the tool. Drilling of carbon fiber reinforced plastic (CFRP) is manageable, but when the minute drill hits the aluminium (Al) or titanium (Ti), the hot and continuous chips produced during machining considerably damage the CFRP hole. This study aims to solve this problem by employing nano-coated drills on multimaterial made of CFRP and aluminium alloy. The influence of cutting parameters on the quality of the holes, chip formation and tool wear were also analyzed. Two types of tungsten carbide drills were used for the present study, one with nano-coating and the other, without nano coating. The experimental results indicated that the shape and the size of the chips are strongly influenced by feed rate. The thrust force generated during drilling of the composite plate with coated drills was 10–15% lesser when compared to that generated during drilling with uncoated drills; similarly, the thrust force in the aluminium alloy was 50% lesser with coated drills when compared to thrust force generated without coated drills. Thus, the use of nano-coated drills significantly reduced the surface roughness and thrust force when compared with uncoated tools.     

Optimization of the mechanical properties of virtual porous solids using a hybrid approach

A hybrid strategy based on artificial neural network/genetic algorithms is suggested to optimize the mechanical properties of cellular solids. Three-dimensional void structures are generated using a random sequential addition algorithm in which spherical void particles are positioned in a solid phase matrix with a control of their size distribution and overlapping. This allows us to create an open-cell structure with relative densities in the range 0.1–0.3. Finite element calculation is used to assess the effective Young’s modulus as a function of generation parameters. Relative Young’s moduli in the three main directions of the solid are isotropic with respect to the generation algorithm constraints and adhere qualitatively to the common theory on effective properties of open-cell structures. Moreover, the effective response is found to be correlated to the randomness of the void structure, which exhibits, in some cases, an ordered cell configuration. In order to quantitatively describe these correlations and to check the sensitivity of the mechanical response to the structural features in addition to sampling and discretization levels, the hybrid strategy described above is considered. The main motivation was to decrease the finite element simulation time by predicting, where possible, the correlations instead of calculating them. In addition, the use of the hybrid strategy informs about the optimal mesh with respect to the generation variables. This strategy proved to be an efficient technique in enhancing the predictions and complementary to the finite element methodology.     

Neural Computation to Estimate Heat Flux in an Atmospheric Plasma Spray Process

Temperature is a key parameter in the thermal spray process and is a consequence of the heat flux experienced by the workpiece. This paper deals with the estimation of the heat flux transmitted to a workpiece from an atmospheric plasma spray torch during the preheating process often implemented in thermal spraying. An inverse heat conduction problem solution using a conjugate gradient method was considered to determine the heat flux starting from a known temperature distribution. Results from the later method were used to train an artificial neural network to discover correlations between selected processing parameters and heat flux.     

Revisiting Johnson and Jackson boundary conditions for granular flows

In this article, we revisit Johnson and Jackson boundary conditions for granular flows. The oblique collision between a particle and a flat wall is analyzed by adopting the classic rigid‐body theory and a more realistic semianalytical model. Based on the kinetic granular theory, the input parameter for the partial‐slip boundary conditions, specularity coefficient, which is not measurable in experiments, is then interpreted as a function of the particle‐wall restitution coefficient, the frictional coefficient, and the normalized slip velocity at the wall. An analytical expression for the specularity coefficient is suggested for a flat, frictional surface with a low frictional coefficient. The procedure for determining the specularity coefficient for a more general problem is outlined, and a working approximation is provided. Published 2011 American Institute of Chemical Engineers, 2012     

Perovskite Formation and Dielectric Responses in PZN Modified PMF-PZT Ceramics

PMF-PZN-PZT （0.01Pb（Mol/3Fe2/3）O3-xPb（Znl/3Nb2/3）O3-（O.99-x）P（Zro53Tio 47）03 piezoelectric ceramics）, where x = 0.00 0.01, 0.03, 0.05 and 0.07 were prepared by a conventional mixed-oxide method. The results show that the pure peroveskit phase forms in these ceramics. X-ray diffraction analysis indicated that the phase of the material is a MPB （morphotropic phase boundary） structure. The effects of PZN content on the crystal structure and electrical properties were investigated, optimal dielectric properties were achieved at composition x = 0.07 ceramics by calcination at 800 ℃ and sintering at 1,180 ℃, with a curie temperature of approximately 430 ℃. These results clearly show the significance of PZN in controlling the electrical responses of the PMF-PZN-PZT system.     

A physically based diode model for circuit simulation

A physically based power PiN diode model is presented. Eigen value internal approximation method is used to solve the ambipolar diffusion equation. This model is implemented in SIMPLORER circuit simulator using VHDL‐AMS language. The proposed model can be used in both circuit simulators and the optimization of a given power PiN diode. Good agreement is obtained by comparing the results of the suggested model with experimental data. Copyright © 2013 John Wiley & Sons, Ltd.     

Constrained model predictive control for time-varying delay systems: Application to an active car suspension

This study investigates the problem of robust model predictive control (RMPC) for active suspension systems with time-varying delays and input constraints. The uncertainty is of convex polytopic type. Based on the Lyapunov-Krasovskii functional method, sufficient stability conditions of the time-varying delays systems are derived by linear matrix inequalities (LMIs) terms. At each time set, a feasible state feedback is obtained by minimizing an upper bound of the ‘worst-case’ quadratic objective function over an infinite horizon subject to constraints on inputs. Finally, a quarter-vehicle model is exploited to demonstrate the effectiveness of the proposed method.     

A Nonparametric Approach to Design Fixed-order Controllers for Systems with Constrained Input

International Journal of Control, Automation and Systems - This paper presents an approach for designing fixed-structure controllers for input-constrained linear systems using frequency domain...     

Pansharpening multispectral remote sensing data by multiplicative joint nonnegative matrix factorization

Pansharpening aims at combining observable panchromatic and multispectral images to generate an unobservable image with the high spatial resolution of the former and the spectral diversity of the latter. In this paper a new fusion method is proposed. This method, related to linear spectral unmixing (LSU) techniques and based on non-negative matrix factorization (NMF), optimizes, by new iterative–multiplicative update rules, a joint criterion that exploits a spatial degradation model between the two images. The proposed Multiplicative Joint Non-negative Matrix Factorization (MJNMF) approach is applied to synthetic and real data, and its effectiveness in spatial and spectral domains is evaluated with commonly used performance criteria. Experimental results show that the proposed method yields good spectral and spatial fidelities of the pansharpened data. Also, it outperforms those tested from the literature.     

Deep mining port scans from darknet

TCP/UDP port scanning or sweeping is one of the most common technique used by attackers to discover accessible and potentially vulnerable hosts and applications. Although extracting and distinguishing different port scanning strategies is a challenging task, the identification of dependencies among probed ports is primordial for profiling attacker behaviors, with a final goal of better mitigating them. In this paper, we propose an approach that allows to track port scanning behavior patterns among multiple probed ports and identify intrinsic properties of observed group of ports. Our method is fully automated based on graph modeling and data mining techniques, including text mining. It provides to security analysts and operators relevant information about services that are jointly targeted by attackers. This is helpful to assess the strategy of the attacker by understanding the types of applications or environment he or she targets. We applied our method to data collected through a large Internet telescope (or darknet).