×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerImpact energy prediction of thermal aged cast stainless steel from impact test was studied using artificial neural network (ANN) modeling. Impact energy data for specimens from eleven cast stainless steel alloys at different aging times and temperatures, were used to evaluate possible artificial neural network architecture for prediction impact energy. These data are taken from Argonne National Laboratories (ANL) in USA that involved impact test results of cast stainless steel after aging between 200 and 400oC for up to 30000 hour. The ANN model exhibited excellent comparison with experimental results of ANL i.e. correlation coefficient (R=0.9451) and mean square error (MSE=1.2*10-5). Since a large number of variables were used during training the ANN model, a reliable and useful predictor for impact energy in thermal aged cast stainless steel was provided.
The dieless drawing process is an innovative method emanated and appeared in coincidence with development of the concept of metal superplasticity. It is utilized from the local heating of a wire or tube to a specified temperature and followed by a local cooling, so an additional deformation is inhibited. In this study, a special dieless drawing machine was designed to carry out an experimental program on SUS304-stainless steel wire having diameter of (1.6-2) mm to investigate the main process parameters such as speeds, heat quantity, heating coil width and heating-cooling separation distance. Also, a numerical model based on thermo-mechanical analysis was developed and validated with experimental program. Furthermore, an artificial neural network ANN model based on current experimental data was prepared to predict the dieless drawing behavior. A maximum area reduction of 40.7% was obtained in single pass. A 3.12mm/s feeding velocity and 4.97mm/s drawing velocity were realized through the experimental tests. The results showed that both drawing force and wire profile were effected by increasing of feeding speed, heating coil width and separation distance. Also, it is confirmed that strain rate was reduced by increasing the heating coil width and the reduction ratio was promoted. A maximum error of 21% was recorded between ANN model and experimental results. The results showed a good agreement among experimental, numerical and ANN models.
An experimental study has been implemented to study the effect of the central radial groove on the bearing pressure distribution. This study is based on the artificial neural network in the prediction of the complex and uncertain positions. Both width and depth of the groove have been varied at some magnitudes in order to investigate their effects on the pressure distribution and the stability of the bearing. Also, the effect of the groove parameters on the noise at the bearing situation in the systems have been analyzed and discussed. The results show that the use of neural network in the prediction of some points with range is very powerful in the minimization of the overall cost of groove design.
In this research, the effect of seawater environments and surface roughness on uniform corrosion rate of carbon steel (A516 grade 65) was studied depending on the experimental work and artificial neural network modeling. The experimental work involves chemical composition, samples machining, roughness measurements (for carbon steel specimens), conductivity and salinity measurements (for seawater), and uniform corrosion test. Weight loss technique was employed in determining the uniform corrosion rate in carbon steel material. Also, artificial neural network (ANN) model was built to predict the values of uniform corrosion rate (mpy) at different values of conductivity, salinity for seawater and roughness factor for carbon steel depending on the experimental results which were used train and test the ANN. The results obtained of uniform corrosion rate by ANN predictions are shown to be agreed well against experimental values. i.e. correlation coefficient, R=0.9974
This paper explores the potential of using artificial neural networks to predict the ultimate moment capacity of steel-concrete composite beams with metal deck slabs. Basic information on artificial neural networks and parameters suitable for the analysis of experimental results are given. A multilayer backpropagation neural network is used for training and testing the experimental data. A comparison study between the experimental values and two models (neural network and AJSC models) is also carried out. It was found that 1he neural network model provides better results. The proposed neural network is also used to explore the effect of the various parameters on the behavior of beams.
This paper introduces a radial distribution feeder protection scheme based on certain features extraction from current signals measurement at the substation. The features are captured using the discrete wavelet transform (DWT). Two digital signals processing methods are used to introduce those features to the 1) fault detection 2) identification and 3) localization schemes; the first one is the energy method and the second one is the root mean square method. For the purpose of fault type identification, two systems are tested and compared, a Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). Fault location scheme is then built based on ANNs. An effort is made to reduce the computational burden and the speed of detection provided by the fault detection and identification schemes. Since the short circuit faults are the most likely types of faults that can occur in power systems, the ten types of these faults taking into account different fault resistances are simulated in MATLAB environment and the protection scheme is built based on the idea of over current. The power quality disturbances such as switching transient events on the feeder is also taken into account in order to build a reliable and secure protection scheme.
The accurate prediction of machinery faults is considered an effective strategy to increase the operation life of machines, ensure smooth operation, and provide a safe environment. Accordingly, the demands on predictive tools such as machine learning to detect machinery faults before catastrophic failure occurs has increased rapidly. In this research, a diagnosis algorithm based on using a 2D color-coded map as the input to a deep artificial neural network is proposed. These maps are called RDEgram after the processing of vibrational signals based on reverse dispersion entropy (RDE) method. The effectiveness of the proposed algorithm is investigated by testing its capability to detect different faults located at different locations on ball bearings under constant speed conditions. First, the squared envelope signal is extracted by applying the short time Fourier transform to vibration signal. Then, the RDE is used to process the squared envelope to detect the range of frequencies at which the transients occur. The RDEgram color-coded map is used to represent the RDE values as a function of frequency and frequency resolution. The maps from different fault features are collected to form the diagnostic patterns. Finally, a pretrained convolutional neural network (CNN) is applied to learn the feature pattern and diagnose the bearing faults. The CNN is trained using fixed- speed data and then it is applied to diagnose faults in the test data recorded at the same speed. The prediction method adopted in the current research shows a 100% level of accuracy for predicting two types of faults (pit and slot) located at various positions a ball bearing (KOYO 1205 C3 type) running at two constant speeds (25 and 30 Hz).
Safwan-Zubair area is regarded as one of the important agricultural areas in Basrah province, South of Iraq. The aim of this study is to predict groundwater level in this area using ANNs model. The data required for building the ANNs model are generated using MODFLOW model (V.5.3). MODFLOW model was calibrated based on field measurements of groundwater level in 13 monitoring wells during a period of one year (Nov./2013 to Oct/2014). The neural network toolbox available in MATLAB version 7.1 (2010B) was used to develop the ANN models. Three layers feed-forward network with Log- sigmoid transfer function was used. The networks were trained using Levenberg-Marquradt back-propagation algorithm. The ANN modes are divided into two groups, each of four models. The input data of the first group include hydraulic heads, while, the input data of the second group include hydraulic heads and recharge rates. Based on results of this study it was found that; the best ANN model for predicting groundwater levels in the study area is obtained when the input data includes hydraulic heads and recharge rates of two successive months preceding the target month, the best structure of ANN model is of three layers feed-forward network type composes of two hidden layers, each of ten nodes, and the including of recharge rates as input data, beside the hydraulic heads has improved slightly the results.
Castor Oil is a natural raw material, used to prepare Brominated Castor Oil (BCO) and quaternary ammonium salt based castor oil (TEt-CO). The two products were tested as demulsifiers and compared with a commercial demulsifier (Chimec2439) by using bottle test method. BCO showed a high ability on water separation efficiency 90% with a dose of 150µl at 120min time settling while TEt- CO showed a low water separation efficiency reached to 10%. The effect of the demulsifier BCO was tested by varying different variables which have an obvious effect on water separation efficiency such as: dose, temperature, time of mixing emulsion, pH and salinity of aqueous phase of emulsion, and water ratio. The effect of some additives (i.e. methanol, ethanol, xylene and toluene) on the efficiency of the BCO was tested for the purpose of enhancing its effectiveness to break the crude oil emulsion. The experimental data obtained by using BCO were formulated as a model using the Artificial Neural Networks (ANNs) to evaluating the water separation efficiency. Multi-layer perceptron artificial neural network was developed based on the collected data of this study. The results showed that the training algorithm of back propagation (BP) is sufficient enough in predicting BCO efficiency under different operation conditions. It was found that the correlation coefficient values are 0.9995 and 0.9999 for the testing and training data, respectively and the mean square error (MSE) was 6.18*10^-5 at 200 epochs.