Sharif Digital Repository

An age-hardening model has been developed to predict the evolution of the hardness of Al-Mg-Si alloys during non-isothermal ageing before peak age. The concurrent precipitation and dissolution have been considered in the structural model. Then the structural model has been combined with strengthening model to predict the precipitation-hardening behavior of the alloy AA6061. The results indicate that the developed model can be used as a predictive tool to model the mechanical properties evolution of Al-Mg-Si alloys during non-isothermal heat treatment. © 2008 Elsevier B.V. All rights reserved  

In this investigation heat treatment optimization of 17-4PH stainless steel has been carried out by a genetic algorithm. The optimum technique of heat treatment, adaptive to 17-4PH stainless steel, was obtained from the initial data set by the use of genetic algorithms based on modeling with artificial neural network. The results strongly indicate that the presented model has the great ability for heat treatment optimization of 17-4PH stainless steel to yield the highest strength levels in different working temperatures. © 2006 Elsevier Ltd. All rights reserved  

In this investigation a theoretical model based on artificial neural network (ANN) has been developed to predict porosity percent and correlate the chemical composition and cooling rate to the amount of porosity in Al-Si casting alloys. In addition, the sensivity analysis was performed to investigate the importance of the effects of different alloying elements, composition, grain refiner, modifier and cooling rate on porosity formation behavior of Al-Si casting alloys. By comparing the predicted values with the experimental data, it is demonstrated that the well-trained feed forward back propagation ANN model with eight nodes in hidden layer is a powerful tool for prediction of porosity... 

In this investigation, a neural network model was used to predict mechanical properties of dual phase (DP) steels and sensivity analysis was performed to investigate the importance of the effects of pre-strain, deformation temperature, volume fraction and morphology of martensite on room temperature mechanical behavior of these steels. In order to train the neural network, dual-phase (DP) steels with different morphology and volume fractions of martensite were deformed between 2 and 8%, at high temperature range of 150-450 °C. The results of this investigation show that there is a good agreement between experimental and predicted values and the well-trained neural network has a great... 

In this paper, an artificial neural network (ANN) model has been developed to predict the yield and tensile strengths of hot pressed NiAl intermetallic compound based on the experimental data from Albiter et al. [A. Albiter, M. Salazar, E. Bedolla, R.A.L. Drew, R. Perez, Mater. Sci. Eng. A 347 (2003) 154]. The predicted results, with a correlation relation between 0.9791 and 0.9921, show a very good agreement with the experimental values. Furthermore, the sensitivity analysis was performed to investigate the importance of the effects of chemical composition and temperature on the mechanical behavior of hot pressed NiAl intermetallic compound. © 2008 Elsevier B.V. All rights reserved  

In this study, the prediction of flow stress in 304 stainless steel using artificial neural networks (ANN) has been investigated. Experimental data earlier deduced-by [S. Venugopal et al., Optimization of cold and warm workability in 304 stainless steel using instability maps, Metall. Trans. A 27A (1996) 126-199]-were collected to obtain training and test data. Temperature, strain-rate and strain were used as input layer, while the output was flow stress. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. The results of this investigation shows that the R2 values for the test and training data set are about...