Showing 5 results for Artificial Neural Network
M. Ghalambaz,, M. Shahmiri, Y. H. K Kharazi,
Volume 4, Issue 1 (6-2007)
Abstract
Abstract: Problems such as the difficulty of the selection of processing parameters and the large
quantity of experimental work exist in the morphological evolutions of Semisolid Metal (SSM)
processing. In order to deal with these existing problems, and to identify the effect of the
processing parameters, (i.e. shearing rate-time-temperature) combinations on particle size and
shape factor, based on experimental investigation, the Artificial Neural Network (ANN) was
applied to predict particle size and shape factor SSM processed Aluminum A.356.0 alloy. The
results clearly demonstrated that, the ANN with 2 hidden layers and topology (4, 2) can predict the
shape factor and the particle size with high accuracy of 94%.The sensivity analysis also revealed
that shear rate and solid fraction had the largest effect on shape factor and particle size,
respectively. The shear rate had a reverse effect on particle size.
Saber Khoshjavan, Mohammad Heidary, Dr Bahram Rezai,
Volume 7, Issue 3 (8-2010)
Abstract
Free swelling index (FSI) is an important parameter for cokeability and combustion of coals. In this research, the effects of chemical properties of coals on the coal free swelling index were studied by artificial neural network methods. The artificial neural networks (ANNs) method was used for 200 datasets to estimate the free swelling index value. In this investigation, ten input parameters such as moisture, volatile matter (dry), fixed carbon (dry), ash (dry), total sulfur (organic and pyretic)(dry), (British thermal unit (Btu)/lb) (dry), carbon (dry), hydrogen (dry), nitrogen (dry) as well as oxygen (dry) were used. For selecting the best model for this study the outputs of models were compared. A three-layer ANN was found to be optimum with architecture of ten and four neurons in first and second hidden layer, respectively, and one neuron in output layer. Results of artificial neural network shows that training, testing and validating data’s square correlation coefficients (R2) achieved 0.99, 0.92 and 0.96, respectively. The sensitivity analysis showed that the highest and lowest effects of coal chemical properties on the coal free swelling index were nitrogen (dry) and fixed carbon (dry), respectively.
Keywords: Coal Chemical Properties, Free Swelling Index, Artificial Neural Networks (ANNs), Cokeability and Back Propagation Neural Network (BPNN).
B. Shahbazi, B. Rezai, S. Chehreh Chelgani, S. M. J. Koleini, M. Noaparast,
Volume 12, Issue 1 (3-2015)
Abstract
Multivariable regression and artificial neural network procedures were used to modeling of the input power
and gas holdup of flotation. The stepwise nonlinear equations have shown greater accuracy than linear ones where
they can predict input power, and gas holdup with the correlation coefficients of 0.79 thereby 0.51 in the linear, and
R2=0.88 versus 0.52 in the non linear, respectively. For increasing accuracy of predictions, Feed-forward artificial
neural network (FANN) was applied. FANNs with 2-2-5-5, and 2-2-3-2-2 arrangements, were capable to estimating of
the input power and gas holdup, respectively. They were achieved quite satisfactory correlations of 0.96 in testing stage
for input power prediction, and 0.64 for gas holdup prediction
E. Maleki, K. Reza Kashyzadeh,
Volume 14, Issue 4 (12-2017)
Abstract
Hardened nickel coating is widely used in many industrial applications and manufacturing processes because of its benefits in improving the corrosion fatigue life. It is clear that increasing the coating thickness provides good protection against corrosion. However, it reduces the fatigue life. Thus, applying a thin layer of coated nickel might give an acceptable corrosion protection with minimum loss of the fatigue life. In the present study, the effects of hardened nickel coating with different thicknesses on the fatigue behavior of CK45 mild steel were experimentally investigated. After conducting the experimental tests, we carried out two different modeling approaches of finite element method (FEM) and artificial neural network (ANN). In the FEM modeling, an attempt was made to analyze the fatigue of the components by modeling the interface phase between the base metal and coating more accurately and using the spring elements; ANNs were developed based on the back propagation (BP) error algorithm. The comparison of the obtained results from FEM and ANN modeling with the experimental values indicates that both of the modeling approaches were tuned finely.
Imtiaz Ali Soomro, Srinivasa Rao Pedapati, Mokhtar Awang, Afzal Ahmed Soomro, Mohammad Azad Alam, Bilawal Ahmed Bhayo,
Volume 19, Issue 4 (12-2022)
Abstract
This paper investigated the optimization, modelling and effect of welding parameters on the tensile shear load bearing capacity of double pulse resistance spot welded DP590 steel. Optimization of welding parameters was performed using the Taguchi design of experiment method. A relationship between input welding paramaters i.e., second pulse welding current, second pulse welding current time and first pulse holding time and output response i.e, tensile shear peak load was established using regression and neural network. Results showed that maximum average tensile shear peak load of 26.47 was achieved at optimum welding parameters i.e., second pulse welding current of 7.5 kA, second pulse welding time of 560 ms and first pulse holding time of 400 ms. It was also found that the ANN model predicted the tensile shear load with higher accuracy than the regression model.
