Mohammad Heidari Articles List

List of Articles Mohammad Heidari

Open Access Article
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1 - Genetic Algorithm and ANN for Estimation of SPIV of Micro Beams
M. Heidari

In this paper, the static pull-in instability (SPIV) of beam-type micro-electromechanical systems is theoretically investigated. Herein, modified strain gradient theory in conjunction with Euler–Bernoulli beam theory have been used for mathematical modeling of the More

In this paper, the static pull-in instability (SPIV) of beam-type micro-electromechanical systems is theoretically investigated. Herein, modified strain gradient theory in conjunction with Euler–Bernoulli beam theory have been used for mathematical modeling of the size dependent instability of the micro beams. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. Two common beam-type systems including double-clamped and clamped-free cantilever have been investigated. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps and size effect, we identify the static pull-in instability voltage. Back propagation artificial neural network (ANN) with three functions have been used for modelling the static pull-in instability voltage of micro beam. Effect of the size dependency on the pull-in performance has been discussed for both micro-structures. The network has four inputs of length, width, gap and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. The number of nodes in the hidden layer, learning ratio and momentum term are optimized using genetic algorithms (GAs). Numerical data, employed for training the network and capabilities of the model in predicting the pull-in instability behaviour has been verified. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the back propagation neural network has the average error of 6.36% in predicting pull-in voltage of cantilever micro-beam. Resultant low relative error value of the ANN model indicates the usability of the BPN in this area. Manuscript profile
Open Access Article
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2 - Faults diagnosis of a girth gear using discrete wavelet transform and artificial neural networks
Mahmuod Akbari Hadi Homaei Mohammad Heidari

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears. DWT is an advanced signal-processing technique for fault detection and identification. Five More

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet coefficients of normalized vibration signals has been selected. These features are considered as the feature vector for training purpose of the ANN. A wavelet selection criteria, Maximum Energy to Shannon Entropy ratio, is used to select an appropriate mother wavelet and discrete level, for feature extraction. To ameliorate the algorithm, various ANNs were exploited to optimize the algorithm so as to determine the best values for ‘‘number of neurons in hidden layer” resulted in a high-speed, meticulous three-layer ANN with a small-sized structure. The diagnosis success rate of this ANN was 100% for experimental data set. Some experimental set of data has been used to verify the effectiveness and accuracy of the proposed method. To develop this method in general fault diagnosis application, an example was investigated in cement industry. At first, a MLP network with well-formed and optimized structure (20:12:3) and remarkable accuracy was presented providing the capability to identify different faults of gears. Then this neural network with optimized structure is presented to diagnose different faults of gears. The performance of the neural networks in learning, classifying and general fault diagnosis were found encouraging and can be concluded that neural networks have high potential in condition monitoring of the gears with various faults. Manuscript profile
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3 - Prediction of Residual Stresses by Radial Basis Neural Network in HSLA-65 Steel Weldments
M. Heidari

This paper investigates the residual stress fields in the vicinity of weld bead in HSLA-65 steel weldments using a neural network. This study consists of two cases: (i) the experimental analysis was carried out on the measurement of residual stresses by XRD technique. M More

This paper investigates the residual stress fields in the vicinity of weld bead in HSLA-65 steel weldments using a neural network. This study consists of two cases: (i) the experimental analysis was carried out on the measurement of residual stresses by XRD technique. Many different specimens that were subjected to different conditions were studied. The values and distributions of residual stresses occurring in welding of HSLA-65 plate under various conditions were determined. (ii) The mathematical modeling analysis has proposed the use of radial basis (RB) NN to determine the residual stresses based on the welding conditions. The input of RBNN are welding current, welding voltage, welding heat input, travel speed of welding, wire feed speed and distance from weld. The best fitting training data set was obtained with 18 neurons in the hidden layer, which made it possible to predict residual stresses with accuracy of at least as good as the experimental error, over the whole experimental range. After training, it was found that the regression values (R2) are 0.999664 and 0.999322 for newrbe and newrb functions respectively. Similarly, these values for testing data are 0.999425 and 0.998505, respectively. Based on the verification errors, it was shown that the radial basis function of neural network with newrbe function is superior in this particular case, and has the average error of 7.70% in predicting the residual stresses in HSLA-65. This method is conceptually straightforward, and it is also applicable to other type of welding for practical purposes. Manuscript profile

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List of Articles Mohammad Heidari