Sharif Digital Repository

Dual-phase steels with different morphology and volume fraction of martensite were deformed between 2% and 8% at a high-temperature range of 150-450 °C. Room-temperature tensile properties showed that both yield and tensile stresses depend on the amount of pre-strain, deformation temperature, volume fraction and morphology of martensite. Results show that both YS and UTS increase with increasing the amount of pre-strain at a given temperature. © 2005 Elsevier B.V. All rights reserved  

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  

The purpose of this investigation is to develop a model for prediction of topologically closed-packed (TCP) phases formation in superalloys. In this study, artificial neural networks (ANN), using several different network architectures, were used to investigate the complex relationships between TCP phases and chemical composition of superalloys. In order to develop an optimum ANN structure, more than 200 experimental data were used to train and test the neural network. The results of this investigation shows that a multilayer perceptron (MLP) form of the neural networks with one hidden layer and 10 nodes in the hidden layer has the lowest mean absolute error (MAE) and can be accurately used... 

Artificial neural network (ANN) and genetic algorithm were used in this study to obtain a relatively high flow stress in compression tests for 304 stainless steel. Cold and warm compression were carried out in a temperature range from 20 to 600 °C, strain-rate from 0.001 to 100 S-1 and a strain range from 0.1 to 0.5. Optimum conditions for each case were obtained experimentally and were evaluated by the ANN model. The ANN model was used as fitness function for genetic algorithm. The results indicated that this combined algorithm offers an effective condition for 304 stainless steel, which avoids flow localization, dynamic strain aging, adiabatic shear deformation and void generation. © 2005... 

In this investigation a theoretical model based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to optimize effective parameters on porosity formation in Al-Si casting alloys. The ANN theory was used to correlate the chemical composition and cooling rate to the amount of porosity. The GA and ANN were incorporated to find the optimal conditions for achieving the minimum porosity percent. By comparing the predicted values with the experimental data - earlier deduced by Dash et al. - it is demonstrated that the combined GA-ANN model is a useful and efficient method to find the optimal conditions for casting of Al-Si alloys associated with the minimum porosity... 

In this study, effects of tungsten on wear resistance of high chromium white cast iron with and without tungsten in erosion-corrosion condition have been investigated. At the same time, the comparison between wear resistance of this grade of cast iron and low alloy steels with various contents of Cr which are used in industrial condition (in Sarcheshme Company, the greatest copper production company in the Middle East and with more than 4000 years historical cupper production background) was studied, while, copper concentrates have used for erosion particles. Results show that, because of higher hardness of matrix due to the tungsten, the wear resistance of high chromium cast iron increases.... 

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... 

In this present research work, a new configuration of the hybrid magnetorheological (MR) brake via T-shaped drum with an arc form surface boundary - the biomechanical geometric design of the hybrid MR brake as a prosthetic knee - is discussed and experimentally tested. The main purpose of this study is to develop a prosthetic knee with one rotary disc to fulfill the desired objective. To achieve this, three steps are considered. In the first step, to model the brake, slab method modeling is used to calculate the braking torque due to the arc surface. In the second step, the biomechanical geometric design is adjusted as an optimization problem to maximize the braking torque, minimize the... 

This paper deals with the study of the small scale effect on the pull-in instability of nano-switches subjected to electrostatic and intermolecular forces. Using Eringen's nonlocal elasticity theory, the nonlocal Euler-Bernoulli beam model is derived through virtual displacement principle. The static governing equation which is extremely nonlinear due to the intermolecular and electrostatic attraction forces is solved numerically by differential quadrature method. The accuracy of the present method is verified by comparing the obtained results with the finite difference method and those in the literatures and very good agreement is obtained. Finally a comprehensive study is carried out to... 

The purpose of this research is to explain the surface modification of fabricated polyamide reverse osmosis (RO) membranes using UV-initiated graft polymerization at different irradiation times (15, 30, 60, and 90 s) and various acrylamide concentrations (10, 20, and 30 g L−1). Also, coating of membranes surface with various concentrations of TiO2 nanoparticles (10, 20, 30, and 50 ppm) followed by the same UV irradiation times was investigated. After that, the membranes modification was done by grafting of acrylamide blended with TiO2 nanoparticles via UV irradiation. The characterization of membranes surface properties and their performance were systematically carried out. The results... 

Considering hydrogen as a future fuel, development of clean approaches based on solar energy conversion is the main human challenge. Here, for the first time, TiO2 photoanodes are decorated with Au-Ag alloy nanoparticles for efficient photoelectrochemical water splitting. The photoanodes were synthesized using a one-step co-sputtering method. The single surface plasmon resonance peak at 540 nm and also the observed shifts in photoelectron binding energies are fingerprints of homogenous alloyed nanoparticles. Scanning electron microscopy revealed the formation of nearly 40 nm particles on the surface, which was also verified by the simulation of the film's optical absorption. Photocurrent... 

Spinal posture including thorax/pelvis orientations as well as loads on the intervertebral discs are crucial parameters in biomechanical models and ergonomics to evaluate the risk of low back injury. In vivo measurement of spinal posture toward estimation of spine loads requires the common motion capture techniques and laboratory instruments that are costly and time-consuming. Hence, a closed loop algorithm including an artificial neural network (ANN) and fuzzy logic is proposed here to predict the L5–S1 segment loads and thorax/pelvis orientations in various 3D reaching activities. Two parts namely a fuzzy logic strategy and an ANN from this algorithm; the former, developed based on the... 

In this paper, a multi-item multiperiod inventory control problem with all-unit and/or incremental quantity discount policies under limited storage capacity is presented. The independent random demand rates of the items in the periods are known and the items are supplied in distinct batch sizes. The cost consists of ordering, holding, and purchasing. The objective is to find the optimal order quantities of all items in different periods such that the total inventory cost is minimized and the constraint is satisfied. A mixed binary integer programming model is first developed to model the problem. Then, a parameter-tuned genetic algorithm (GA) is employed to solve it. Since there is no... 

In recent years, many efforts have been made in the field of computational modeling of cancerous tumors, in order to obtain a better understanding and predictions of their growth patterns. Furthermore, constraint-based modeling of metabolic networks has become increasingly popular, which is appropriate for the systems-level reconstruction of cell physiology. The goal of the current study is to integrate a multiscale agent-based modeling framework with a constraint-based metabolic network model of cancer cells in order to simulate the three dimensional early growth of avascular tumors. In order to develop the integrated model, a previously published generic metabolic network model of cancer... 

The control of a brachiation robot has been the primary objective of this study. A brachiating robot is a type of a mobile arm that is capable of moving from branch to branch similar to a long-armed ape. In this paper, to minimize the actuator work, Pontryagin's minimum principle was used to obtain the optimal trajectories for two different problems. The first problem considers “brachiation between fixed branches with different distance and height,” whereas the second problem deals with the “brachiating and catching of a moving target branch”. Theoretical results show that the control effort in the proposed method is reduced by 25% in comparison with the “target dynamics” method which was... 

In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are...