Wear Performance of Laser Processed Tantalum Coatings

This first generation investigation evaluates the in vitro tribological performance of laser-processed Ta coatings on Ti for load-bearing implant applications. Linear reciprocating wear tests in simulated body fluid showed one order of magnitude less wear rate, of the order of 10(-4)mm(3)(N.m)(-1), for Ta coatings compared to Ti. Our results demonstrate that Ta coatings can potentially minimize the early-stage bone-implant interface micro-motion induced wear debris generation due to their excellent bioactivity comparable to that of hydroxyapatite (HA), high wear resistance and toughness compared to popular HA coatings.     

A production-recycling-inventory model with learning effect

This paper develops an integrated production-recycling system over a finite time horizon. Here, the dynamic demand is satisfied by production and recycling. The used units are bought back and then either recycled or disposed of which are not repairable. The used units are collected continuously from the customers. Recycling products can be used as new products which are sold again. The rate of production and disposal are assumed to be function of time. The setup cost is reduced over time due to “Learning curve” effect. The optimum results are presented both in tabular form and graphically.     

Solving conflicting bi-objective facility location problem by NSGA II evolutionary algorithm

This paper focuses on the facility location problem with two conflicting objectives. We observe that minimization of the total cost of a particular echelon may lead to the increase in the total cost of a supply chain as a whole. Thus, these conflicting objectives are required to be met together from a supply chain perspective. We have solved the problem formulated in mixed nonlinear programming by a multi-objective evolutionary algorithm (MOEA) known as non-dominated sorting algorithm, or NSGA II in short. Numerical example is provided to show the effect of the algorithm on the solution.     

Experimental and FE analysis of submerged arc weld induced residual stress and angular deformation of single and double sided fillet welded joint

Submerged arc welding is well-known for its very high deposition rate and thus the capability to join very high thickness metal pieces in large structural applications. Fillet joints are mostly used in structural applications which can be extensively seen in shipbuilding, bridge construction, house buildings, automobile or any other large structures. Thermal stresses generates in a fusion welded joint due to high temperature gradient, which is the cause of the residual stresses upon cooling followed by the angular deformation and failure of the welded structure. As an effect of the thermal gradient, the induced longitudinal, transverse residual stress & angular distortion can vary in single sided and double sided submerged arc welded fillet joints, during designing & manufacturing welded structures which should be taken into account. The main objective of this paper is to quantify the amount of residual stresses and angular deformation in a fillet welding joint. An elasto-plastic thermomechanical model has been developed for predicting residual stresses. A comparison of the residual stress and angular deformation between single and double sided fillet weld joint has been made. The simulation results reveal that the amount of residual stress present in the single sided fillet weld is more and unbalanced in both side of the center of weld line compared to the double sided fillet weld and the predicted results have been matched with the experiments as well as published literatures.     

Iron-Doped,Mullite-Impregnated PVDF Composite: An Alternative Separator for a High Charge Storage Ceramic Capacitor

In this communication, the formation mechanism of the electroactive β phase, morphology and the dielectric activities of increasing doping concentration (0–1.2 M.W % of mullite) of Fe²⁺ ion-doped, mullite-impregnated polyvinylidene fluoride (PVDF) nanocomposite have been investigated. Differential thermal analysis (DTA) confirms the formation of an electroactive β phase, and Fourier transform infrared spectroscopy (FTIR) showed that the β phase increases simultaneously and attains the maximum increment of 2.6 times compared to pristine PVDF. X-ray diffraction (XRD) spectra also agreed well with the β-phase increment behaviour and also confirmed the presence of required mullite phases. Field emission scanning electron microscopy (FESEM) images indicate the strong interaction between the polymer matrix and different concentrations of Fe²⁺ ion-doped mullite particles, resulting in enhanced electroactive β phase formation and large dielectric constant of the nanocomposite films followed by significant low dielectric loss with high ac conductivity compared to pristine PVDF films at room temperature. This doped polymer composite can be used as a high dielectric separator and, using this separator, we have successfully fabricated a high-charge-storage device. This paper also demonstrates that the loading of conductive Fe²⁺ ions within the highly insulating mullite matrix has a critical concentration for the enhancement and nucleation of the electroactive β phase of the PVDF polymer. In this critical concentration, the highest formation of a β network and maximum numbers of homogeneously distributed iron-doped mullite (FeM) particles in PVDF matrix improves the effective interfacial polarization by Maxwell–Wagner–Sillar (MWS) polarization effect which is responsible for the enhancement of dielectric constant and ac conductivity followed by significant tangent loss. So, it can be concluded that the incorporation of Fe²⁺-doped mullite into PVDF matrix is an effective way to fabricate a high dielectric separator of high-charge-storage electronic devices.     

Temperature dependent growth and optical properties of SnO<Subscript>2</Subscript> nanowires and nanobelts

SnO₂ nanowires and nanobelts have been grown by the thermal evaporation of Sn powders. The growth of nanowires and nanobelts has been investigated at different temperatures (750–1000°C). The field emission scanning electron microscopic and transmission electron microscopic studies revealed the growth of nanowires and nano-belts at different growth temperatures. The growth mechanisms of the formation of the nanostructures have also been discussed. X-ray diffraction patterns showed that the nanowires and nanobelts are highly crystalline with tetragonal rutile phase. UV-visible absorption spectrum showed the bulk bandgap value (∼ 3–6 eV) of SnO₂. Photoluminescence spectra demonstrated a Stokes-shifted emission in the wavelength range 558–588 nm. The Raman and Fourier transform infrared spectra revealed the formation of stoichiometric SnO₂ at different growth temperatures.     

A neural-network-based space-vector PWM controller for athree-level voltage-fed inverter induction motor drive

A neural-network-based implementation of space-vector modulation (SVM) of a three-level voltage-fed inverter is proposed in this paper that fully covers the linear undermodulation region. A neural network has the advantage of very fast implementation of an SVM algorithm, particularly when a dedicated application-specific IC chip is used instead of a digital signal processor (DSP). A three-level inverter has a large number of switching states compared to a two-level inverter and, therefore, the SVM algorithm to be implemented in a neural network is considerably more complex. In the proposed scheme, a three-layer feedforward neural network receives the command voltage and angle information at the input and generates symmetrical pulsewidth modulation waves for the three phases with the help of a single timer and simple logic circuits. The artificial-neural-network (ANN)-based modulator distributes switching states such that neutral-point voltage is balanced in an open-loop manner. The frequency and voltage can be varied from zero to full value in the whole undermodulation range. A simulated DSP-based modulator generates the data which are used to train the network by a backpropagation algorithm in the MATLAB Neural Network Toolbox. The performance of an open-loop volts/Hz speed-controlled induction motor drive has been evaluated with the ANN-based modulator and compared with that of a conventional DSP-based modulator, and shows excellent performance. The modulator can be easily applied to a vector-controlled drive, and its performance can be extended to the overmodulation region     

High stress abrasive wear behavior of sillimanite-reinforced Al-alloy matrix composite: A factorial design approach

An attempt has been made to explore the possibility of using a natural mineral, namely sillimanite, as dispersoid for synthesizing aluminum alloy composite by solidification technique. The abrasive wear behavior of this composite has been studied through factorial design of experiments. The wear behavior of the composite (Y _composite) and the alloy (Y _alloy) is expressed in terms of the coded values of different experimental parameters like applied load (x ₁), abrasive size (x ₂), and sliding distance (x ₃) by the following linear regression equations:

Optimization study of adsorption parameters for removal of phenol on gastropod shell dust using response surface methodology

Phenolics have recently been of great concern because of the extreme toxicity and persistence in the environment. This study explores the possibility of using gastropod shell dust (GPSD) to remove phenol from aqueous solutions. The removal of phenol was investigated in batch mode. The influence of different experimental parameters—initial pH, adsorbent dose, initial concentration, contact time, stirring rate, temperature, and their interaction during phenol adsorption—were determined by response surface methodology based on three-level four-factorial Box–Behnken design. Optimized values of initial phenol concentration, pH, adsorbent dose, and contact time were found as 10.16 mg/L, 4.22, 0.50 g/L, and 33.47 min, respectively. The experimental equilibrium data were tested by four widely used isotherm models namely, Langmuir and Freundlich, D–R, and Temkin. It was found that adsorption of phenol on gastropod shell dust correlated with the Langmuir isotherm model, implying monolayer coverage of phenol onto the surface of the adsorbent. The maximum adsorption capacity was found to be 56.89 mg g^?1 at 333 K. Regeneration study revealed that about 92 % phenol can be regenerate within 90 min from the spent GPSD. Kinetics of the adsorption process was tested by pseudo-first-order, pseudo-second-order kinetics, and intra-particle diffusion mechanism. Pseudo-second-order kinetic model provided a better correlation for the experimental data studied in comparison to the pseudo-first-order model. Intra-particle diffusion was not the sole rate-controlling factor. The activation energy of the adsorption process (E _a) was found to be 2.68 kJ mol^?1, indicating physisorption nature of phenol adsorption onto gastropod shell dust. A thermodynamic study showed spontaneous nature and feasibility of the adsorption process. A negative enthalpy (ΔH°) value indicated that the adsorption process was exothermic. The results revealed that gastropod shell dust can be used as an effective and low-cost adsorbent to remove phenol from aqueous solutions.     

Change detection for moving object segmentation with robust background construction under Wronskian framework

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.