Journal of Advanced Materials in Engineering (Esteghlal)

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Production of nickel-silver by mechanical alloying was investigated. Effects of parameters such as milling duration, ball to powder weight ratio, and chemical composition on mechanical alloying process, and alloy's color and microstructure were studied. The milled powders were characterized, using XRD and SEM. Results showed that nickel-silvers could be produced by mechanical alloying in a wide range of compositions. Alloyed powder with a bright silvery contrast and less than 15 nm grain size could be obtained by optimization of milling parameters. Zinc content of the powder mixture had a significant effect on the minimum alloying time. Ball to powder ratio up to 25 also reduced minimum alloying time but it had no significant effect above this value.

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Polypropylene (PP) has poor adhesion to metals and other surfaces for its chemical structure. Hence, chemical modification of PP is necessary for metal surface coating application. In this research, grafting of maleic anhydride (MA) onto co(propylene-b-ethylene) in the presence of a dicumyl peroxide (DCP) was accomplished in a single screw extruder. Characteristics of the modified polymer were determined by Infra-red Spectroscopy (IR), Scanning Electron Microscopy (SEM), and adhesion test. Maximum grafting of MA was found to be 1.2832% for 1.5 pph of MA. Adhesion test showed that the samples containing 1 pph of MA (degree of grafting is 0.5816%) had better adhesion to steel surface (17.25 kgf).

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The aim of this research is to design a controlling loop that eliminates the irregularities in yarn tension during the winding process. In order to achieve this, we employed a relative feedback industrial control system. The yarn tension sensor measures the tension. Its output is analyzed in the automatic controlling unit. This unit adjusts the tension level according to feedback signals, thus adjusting the yarn tension to the desired value. The yarn package wound using this system will additionally experience the least yarn tension variations.

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Water-hammer is a transient condition which may occur in a network as a result of rapid or slow valve closures, pump failures, changes in turbine loading, etc. It creates high and low pressure waves which travel along the system and decay as a result of wall shear stress. Comparison o experimental and theoretical results revealed the failure of steady or quasi-steady models in correctly predicting the daming process of the pressure waves. In fact, the velocity profiles have greater gradients under unsteady conditions which results in higher shear stresses compared to the steady condition. In this paper, the transient condition in a network (valve-pipe-tank system) is investigated by implementing one of the unsteady friction models (Brunone model) into the method of characteristics (MOC). Results show that using the unsteady friction model damps the pressure waves more rapidly, the absence of which may result in disagreement between theoretical and experimental values. In addition, this work shows that pressure rise due to the water hammer phenomenon cannot be correctly determined without effecting the unsteady friction factor. The valve closure law affects pressure rise prediction.

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A systolic serial multiplier for unsigned numbers is presented which operates without zero words inserted between successive data words, outputs the full product and has only one clock cycle latency. The multiplier is based on a modified serial/parallel scheme with two adjacent multiplier cells. Systolic concept is a well-known means of intensive computational task through replication of functional units and their repetitive use. Digital signal processing applications often involve high-speed sequential data. Bit-serial processing in particular can result in efficient communications, both within and between VLSI chips because of the reduced number of interconnections required. Serial input multipliers have received considerable attention, particularly for hardwired VLSI algorithms used in signal processing application, due to their minimal chip area required for interconnections. Bit-serial architectures are often used in parallel systems with high connectivity to reduce the wiring down to a reasonable level. The conventional add-shift technique for multiplication, which uses a minimum number of gates, is inexpensive to implement, but too slow to achieve the desired result. Iterative array multipliers are needed to satisfy the high speed requirement of systems. With the advantage of high scale integration, the hardware is not regarded as a major obstacle in implementation.

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In welded tubular joints, when the fatigue crack depth is less than 20% of chord wall thickness, the crack growing process is highly affected by weld geometry. Hence, T-butt solution and weld magnification factor (Mk) are applicable tools for evaluating the crack growth rate in this domain. In this research, the capability of Artificial Neural Network (ANN) for estimating the Mk of weld toe cracks in T-butt joints is investigated. Four Multi-Layer Perceptron (MLP) networks are designed and trained to predict the Mk in deepest point and ends of weld toe cracks under membrane and bending stresses. Training and testing data of networks are extracted from a reputable resource on finite element modeling. Comparison of the results obtained and those from the most recently published equations shows that using ANN seems to be very beneficial in this field

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The reflectance factors of transparent fibers, free delustering agent, are predicted by geometric as well as Kubelka-Munk models. Transparent fibers are simulated by a net of glass capillary tubes containing different solutions of dyestuffs. Based on the results, prediction of the reflectance factor of capillary net by geometric model is relatively better than those obtained from Kubelka-Munk model. However, the geometric model suffers from a complex and massive computation process. Generally speaking, the geometric model performs better for dark transparent samples due to the ignorable internal scattering phenomena. On the other hand, the Kubelka-Munk model provides better results for light samples, where the geometric model fails in acceptable prediction.

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In this paper, the problem of batch scheduling in a flexible flow shop environment is studied. It is assumed that machines in some stages are able to process a number of jobs simultaneously. The applications of this problem can be found in various industries including spring and wire manufacturing and in auto industry. A mixed integer programming formulation of the problem is presented and it is shown that the problem is NP-Hard. Three heuristics will then be developed to solve the problem and a lower bound is also developed for evaluating the performance of the proposed heuristics. Results show that heuristic H3 gives better results compared to the others.

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In this paper, two suboptimum detectors are proposed for coherent radar signal detection in K-distributed clutter. Assuming certain values for several initial moments of clutter amplitude, the characteristic function of the clutter amplitude is approximated by a limited series. Using the Pade approximation, it is then converted to a rational fraction. Thus, the pdf of the clutter amplitude is obtained as a sum of simple exponential functions. Using such a pdf, we develop the suboptimum detectors PGLR and PAALR, which are simplified forms of the GLR and AALR. Computer simulations show that the suggested detectors have appropriate performance compared to OLD, GLR and AALR detectors.

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Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need large amounts of historical data. Although fuzzy forecasting models such as fuzzy regression are suitable metods when the data available is scant, their performance is not satisfactory at times. In this paper, a new Fuzzy Auto Regressive Integrated Moving Average (FARIMA) is presented. The proposed model can be run with less data, so it is more suitable than other models for cases where there are limited data available. The results obtained on exchange rate forecasting reveal the efficiency of the proposed model.

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The mechanical behavior of cold rolled sheets is significantly related to residual stresses that arise from bending and unbending processes. Measurement of residual stresses is mostly limited to surface measurement techniques. Experimental determination of stress variation through thickness is difficult and time-consuming. This paper presents a closed form solution for residual stresses, in which the bending-unbending process is modeled as an elastic-plastic plane strain problem. An anisotropic material is assumed. To validate the analytical solution, finite element simulation is also demonstrated. This study is applicable to analysis of coiling-uncoiling, leveling and straightening processes.

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This paper tries to estimate the capital investment required for the fixed-telephony network switching equipment as demanded by the fourth national development plan. As a first step, the Cobb-Douglas model is used as a successful demand forecasting model to estimate the demand over the target years. Then, an architectural plan is developed for the fixed-telephony switching network that takes into account the expansion of the existing exchanges as well as the addition of new ones. The number of the required ports in local exchanges, the intercity traffic (including cell phone subscribers), and the required trunks in transit exchanges are then estimated. Two scenarios are used to estimate the investment needed: expanding legacy network (circuit-based), and NGN adoption (a combination of circuit and packet-based networks). Finally, conventional pricelists from different local and foreign suppliers are used to arrive at two total investment estimates: 6,013 billion Rials and 6330 billion Rials for the two mentioned scenarios, respectively.

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In this paper, radar detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like the GLRT method). In the second detector, the averaged likelihood ratio is calculated by integrating out the unknown parameters (like the AALR method). Thanks to the numerical nature of these methods, they can be applied to many detection problems which do not have analytical solutions. Simulation results show that both the proposed detectors and the GLRT have approximately the same performance in problems to which the GLRT can be applied. On the other hand, the proposed detectors can be used in many cases for which either no ML estimate of unknown parameters exists or their prior distribution is known.

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This paper presents a nonlinear mixed-integer programming model to minimize the stoppage cost of mixed-model assembly lines. Nowadays, most manufacturing firms employ this type of line due to the increasing varieties of products in their attempts to quickly respond to diversified customer demands. Advancement of new technologies, competitiveness, diversification of products, and large customer demand have encouraged practitioners to use different methods of improving production lines. Minimizing line stoppage is regarded as a main factor in determining the sequence of processing products. Line stoppage results in idleness of operators and machines, reduced throughput, increased overhead costs, and decreased overall productivity. Due to the complexity of the model proposed, which belongs to a class of NP-hard problems, a meta-heuristic method based on a genetic algorithm (GA) is proposed to obtain near-optimal solutions in reasonable time, especially for large-scale problems. To show the efficiency of the proposed GA, the computational results are compared with those obtained by the Lingo software.

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In this paper, an elastoplastic constitutive model is presented for predicting sandy soil behavior under monotonic and cyclic loadings. The model is based on the CJS3 model that takes into account deviatoric and isotropic mechanisms of plasticity. The flow rule in deviatoric mechanism is non-associated and a kinematic hardening law controls the evolution of the yield surface. In the present study, the critical state surface and history surface separating the virgin and cyclic states in the stress space are defined. Hardening modulus and stress-dilatancy law for monotonic and cyclic loadings are effectively modified. Taking the hardening modulus as a function of deviatoric and volumetric plastic strain, the model will be capable of predicting sand behavior once the history surface and stress reversal are defined. All model parameters have clear physical meanings and can be determined simply from laboratory tests. The results of homogeneous tests on Hostun sand are used to validate the model. The results of validation indicate the capability of the proposed model.