Experimental and Theoretical Investigations of Mouldability for Feedstocks Used in Powder Injection Moulding

Experimental and theoretical analyses of mouldability for feedstocks used in powder injection moulding are performed. This study covers two main analyses. (i) The experimental analysis: the barrel temperature, injection pressure, and flow rate are factors for powder injection moulding (PIM). Powder-binder mixture used as feedstock in PIM requires a little more attention and sensitivity. Obtaining the balance among pressure, temperature, and especially flow rate is the most important aspect of undesirable conclusions such as powder-binder separation, sink marks, and cracks in moulded party structure. In this study, available feedstocks used in PIM were injected in three different cavities which consist of zigzag form, constant cross-section, and stair form (in five different thicknesses) and their mouldability is measured. Because of the difference between material and binder, measured lengths were different. These were measured as 533 mm, 268 mm, 211 mm, and 150 mm in advanced materials trade marks Fe–2Ni, BASF firm Catamould A0-F, FN02, and 316L stainless steel, respectively. (ii) The theoretical analysis: the use of artificial neural network (ANN) has been proposed to determine the mouldability for feedstocks used in powder injection moulding 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 three and four neurons in the hidden layer, which made it possible to predict yield length with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found that the R2 values are 0.999463, 0.999445, 0.999574, and 0.999593 for Fe–2Ni, BASF firm Catamould A0-F, FN02, and 316L stainless steel, respectively. Similarly, these values for testing data are 0.999129, 0.999666, 0.998612, and 0.997512, respectively. As seen from the results of mathematical modeling, the calculated yield lengths are obviously within acceptable uncertainties

Performance maps of a diesel engine

This 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

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000250320800019The most important theme in this study is to obtain equations based on economic indicators (gross national product - GNP and gross domestic product - GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968-2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R 2 values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections. (D 2007 Elsevier Ltd. All rights reserved

Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000241706500023This paper proposes artificial neural networks (ANNs) technique as a new approach to determine the exergy losses of an ejector-absorption heat transformer (EAHT). Thermodynamic analysis of the EAHT is too complex due to complex differential equations and complex simulations programs. ANN technique facilitates these complicated situations. This study is considered to be helpful in predicting the exergetic performance of components of an EAHT prior to its setting up in a thermal system where the working temperatures are known. The best approach was investigated using different algorithms with developed software. The best statistical coefficient of multiple determinations (R-2-value) for training data equals to 0.999715, 0.995627, 0.999497, and 0.997648 obtained by different algorithms with seven neurons for the non-dimensional exergy losses of evaporator, generator, absorber and condenser, respectively. Similarly these values for testing data are 0.999774, 0.994039, 0.999613 and 0.99938, respectively. The results show that this approach has the advantages of computational speed, low cost for feasibility, rapid turnaround, which is especially important during iterative design phases, and easy of design by operators with little technical experience. (c) 2006 Elsevier Ltd. All rights reserved

Artificial neural network investigation of hardness and fracture toughness of hydroxylapatite

Hardness and fracture toughness of hydroxylapatite were investigated by artificial neural network (ANN). Hardness and fracture toughness of hydroxylapatite were predicted by using its sintering temperature, sintering time, relative density, and grain size with ANN. It was found that prediction results of its hardness and fracture toughness closely matched with the experimental results. (C) 2010 Elsevier Ltd and Techna Group S.r.l. All rights reserved

Artificial neural network application to the friction-stir welding of aluminum plates

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000242785600009An 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

Predictions of temperature distributions on layered metal plates using artificial neural networks

The temperature distribution influences the amount of energy needed to heat a body. The benefits of using multi-layered metal plates (NIMP) are due to the requirement of a regular temperature distribution on the opposite side with one side heated irregularly. The factors that affect the regular distribution of the temperature in such a structure are the thickness of the layers and the materials themselves, since for different materials, heat conduction coefficients, density and specific heat values change

Derivation of empirical equations for thermodynamic properties of a ozone safe refrigerant (R404a) using artificial neural network

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000272432300031This 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

Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000251630100043Recently, 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