Journal of Advanced Materials in Engineering (Esteghlal)

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Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of cotton-covered nylon core yarns. Multilayer Feedforward network with Back Propagation learning algorithm was used to study the relationship and mapping among the process parameters, i.e. count of sheath part, count of core part, applying pretension to the core part, inserted twist to the core spun-yarn as well as tensile properties, i.e. breaking strength and breaking elongation. The results show that ANN is an effective method for the prediction of the tensile properties of these yarns. This is due to the fact that in each case, standard deviation of prediction error for test and train data was less than that obtained from the expreiments.

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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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Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an effort to investigate the capability of artificial neural network in estimatiing the time to failure for SCC of 304 stainless steel in aqueous chloride solution and to provide a sensitivity analysis thereof. The input parameters considered are temperature, chloride ion concentration, and applied stress. The time to failure is defined as the output parameter and the key criterion to evaluate the effective parameters. The statistical performance of the neural network is expressed as the average of three learning and testing results. The SCC database is divided into two sections designated as the learning set and the testing set. The output results show that artificial neural network can predict the time to failure for about 74% of the variance of SCC experimental data. Furthermore, the sensitivity analysis also exhibits the effects of input parameters on SCC of 304 stainless steel in aqueous chloride solutions.

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Mechanical alloying technique is used for production of nanostructured soft magnetic alloys. In this work the back propagation (BP) artificial neural adopted to model the effect of various mechanical alloying parameters i.e. milling time and chemical composition, on the properties of Fe-Ni powders. Lattice parameter, grain size, lattice strain, coersivity and saturation intrinsic flux density are considered as the output of five BP neural networks. The results obtained show the efficiency of designed networks for the prediction of the properties of Fe-Ni powders.