1,721,009 research outputs found
Artificial neural network application to the friction-stir welding of aluminum plates
No full textARCAKLIOGLU, Erol/0000-0001-8073-5207An artificial neural network (ANN) model was developed for the analysis and simulation of the correlation between the friction stir welding (FSW) parameters of aluminium (Al) plates and mechanical properties. The input parameters of the model consist of weld speed and tool rotation speed (TRS). The outputs of the ANN model include property parameters namely: tensile strength, yield strength, elongation, hardness of weld metal and hardness of heat effected zone (HAZ). Good performance of the ANN model was achieved. The model can be used to calculate mechanical properties of welded Al plates as functions of weld speed and TRS. The combined influence of weld speed and TRS on the mechanical properties of welded Al plates was simulated. A comparison was made between measured and calculated data. The calculated results were in good agreement with measured data. (c) 2005 Elsevier Ltd. All rights reserved
Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies
No full textARCAKLIOGLU, Erol/0000-0001-8073-5207Recently, global warming and its effects have become one of the most important themes in the world. Under the Kyoto Protocol, the EU has agreed to an 8% reduction in its greenhouse gas (GHG) emissions by 2008-2012. The GHG emissions (total GHG, CO2, CO, SO2, NO2, E (emissions of non-methane volatile organic compounds)) covered by the Protocol are weighted by their global warming potentials (GWPs) and aggregated to give total emissions in CO2 equivalents. The main subject in this study is to obtain equations by the artificial neural network (ANN) approach to predict the GHGs of Turkey using sectoral energy consumption. The equations obtained are used to determine the future level of the GHG and to take measures to control the share of sectors in total emission. According to ANN results, the maximum mean absolute percentage error (MAPE) was found as 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for GHG, SO2, NO2, CO, E, and CO2, respectively, for the training data with Levenberg-Marquardt (LM) algorithm by 8 neurons. R-2 values are obtained very close to 1. Also, this study proposes mitigation policies for GHGs. (C) 2007 Elsevier Ltd. All rights reserved
Derivation of empirical equations for thermodynamic properties of a ozone safe refrigerant (R404a) using artificial neural network
No full textARCAKLIOGLU, Erol/0000-0001-8073-5207This study, deals with the potential application of the artificial neural networks (ANNs) to represent PVTx (pressure-specific volume-temperature-vapor quality) data in the range of temperature of 173-498 K and pressure of 10-3600 kPa. Generally, numerical equations of thermodynamic properties are used in the computer simulation analysis instead of analytical differential equations. And also analytical computer codes usually require a large amount of computer power and need a considerable amount of time to give accurate predictions Instead of complex rules and mathematical routines, this study proposes an alternative approach based on ANN to determine the thermodynamic properties of an environmentally friendly refrigerant (R404a) for both saturated liquid-vapor region (wet vapor) and superheated vapor region as numerical equations. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and effort. R-2 values which are errors known as absolute fraction of variance - in wet vapor region are 0.999401, 0 999982 and 0.999993 for specific volume. enthalpy and entropy for training data, respectively. For testing data, these values are 0.998808. 0.999988, and 0 999993 Similarly, for superheated vapor region, they are 0.999967, 0.999999 and 0.999999 for training data, 0.999978, 0.999997 and 0.999999 for testing data. As seen from the results of mathematical modeling, the calculated thermodynamic properties are obviously within acceptable uncertainties. (C) 2009 Elsevier Ltd. All rights reserved
Modelling of yield length in the mould of commercial plastics using artificial neural networks
No full textARCAKLIOGLU, Erol/0000-0001-8073-5207;According to the yield length of a plastic, whether a mould is filled fully or not can be estimated. In this study, a new formula based on various injection parameters was developed to determine the yield length in plastic moulding of the commercial plastics, the most widely used ones are low-density polyethylene, high-density polyethylene, polystyrene and polypropylene, by artificial neural network (ANN). This study covers two main objectives: (i) the yield properties of plastics have been investigated at various injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould including spiral chutes as experimental; (ii) the yield length of the commercial plastics based on various measured injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould was described by ANN using experimental data. Some experimental data were used as test data, and these values were not used for training. Scaled conjugate gradient and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. The results show that the maximum mean absolute percentage error (MAPE) was found to be 1.574969% and R-2 values 0.99964. The best approach was found in the LM algorithm with six neurons. The MAPE and R-2 for testing data were 1.849 and 0.9995, respectively, similar to algorithm and neurons. This study is considered to be helpful in predicting the yield length in the mould whose injection parameters are known. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs. (c) 2005 Elsevier Ltd. All rights reserved
Determination of residual stresses based on heat treatment conditions and densities on a hybrid (FLN2-4405) powder metallurgy steel using artificial neural network
No full textARCAKLIOGLU, Erol/0000-0001-8073-5207This paper presents a new approach based on artificial neural networks (ANNs) to determine the residual stresses in PM steel based nickel (FLN2-4405). This study consists of two cases: (i) The experimental analysis: The measurements of residual stresses were carried out by electrochemical layer removal technique. The values and distributions of residual stresses occurring in PM steel processed under various densities (6.8, 7.05, 7.2 and 7.4 g/cm(3)) and heat treatment conditions (sintered at 2050 degrees F, sintered at 2300 degrees F, quenching-tempered, and sinter-hardened) were determined. In most of the experiments, tensile residual stresses were recorded in surface of samples. The residual stress distribution on the surface of the PM steels is affected by the heat treatment conditions and density. Maximum values of residual stresses on the surface were observed sinter hardened condition and 7.4 g/cm(3) density. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the residual stresses based on heat treatment conditions and densities in PM steel using results of experimental analysis. The back propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four and five neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R 2 values are 0.999244, 0.999025, 0.999664 and 0.999322 for sintered at 2050 degrees F, sintered at 2300 degrees F, quenching-tempered, and sinter-hardened, respectively. Similarly, these values for testing data are 0.998354, 0.99706, 0.999607 and 0.999205, respectively. As seen from the results of mathematical modeling, the calculated residual stresses are obviously within acceptable uncertainties. (c) 2006 Elsevier Ltd. All rights reserved
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey
No full textVariations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Performance maps of a diesel engine
No full textThis paper suggests a mechanism for determining the constant specific-fuel consumption curves of a diesel engine using artificial neural-networks (ANNs). In addition, fuel-air equivalence ratio and exhaust temperature values have been predicted with the ANN. To train the ANN, experimental results have been used, performed for three cooling-water temperatures 70, 80, 90, and 100 °C for the engine powers ranging from 1000 to 2300 - for six different powers of 75-450 kW with incremental steps of 75 kW. In the network, the back-propagation learning algorithm with two different variants, single hidden-layer, and logistic sigmoid transfer function have been used. Cooling water-temperature, engine speed and engine power have been used as the input layer, while the exhaust temperature, break specific-fuel consumption (BSFC, g/kWh) and fuel-air equivalence ratio (FAR) have also been used separately as the output layer. It is shown that R2 values are about 0.99 for the training and test data; RMS values are smaller than 0.03; and mean errors are smaller than 5.5% for the test data.Artificial neural-network Performance maps Fuel-air equivalence ratio Diesel engine
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