Parya Aghasafari | The University of Georgia (original) (raw)
Address: Athens, United States
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Papers by Parya Aghasafari
In this study, an artificial neural network model is proposed to predict the flow stress variatio... more In this study, an artificial neural network model is proposed to predict the flow stress variations during
the hot rolling process. Optimization of the proposed neural network with respect to number of neurons
within the hidden layer, different training methods and transfer functions of the neural network is performed.
The results of the optimal network were compared with those of the conventional analytic
method and it is shown that using an optimal neural network the mean calculated error is drastically
reduced.
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In this paper, an inverse analysis technique is used to obtain the flow curve of materials in a h... more In this paper, an inverse analysis technique is used to obtain the flow curve of materials in a hot rolling
finishing mill. This technique is based on minimization of the differences between the experimental and
computed values. The flow curves and the friction coefficients at roll/work-piece interface are derived from
two different models. Model I is based on simple slab method of analysis. Model II is based on a modified
slab method in which the effect of shear stress in calculating the rolling force and torque is taken into
account. It is shown that the developed inverse analysis technique is reliable and can simultaneously
determine a more accurate flow stress for the material as well as a better estimation for the interface friction
factors.
Bookmarks Related papers MentionsView impact
In this study, an artificial neural network model is proposed to predict the flow stress variatio... more In this study, an artificial neural network model is proposed to predict the flow stress variations during
the hot rolling process. Optimization of the proposed neural network with respect to number of neurons
within the hidden layer, different training methods and transfer functions of the neural network is performed.
The results of the optimal network were compared with those of the conventional analytic
method and it is shown that using an optimal neural network the mean calculated error is drastically
reduced.
Bookmarks Related papers MentionsView impact
In this paper, an inverse analysis technique is used to obtain the flow curve of materials in a h... more In this paper, an inverse analysis technique is used to obtain the flow curve of materials in a hot rolling
finishing mill. This technique is based on minimization of the differences between the experimental and
computed values. The flow curves and the friction coefficients at roll/work-piece interface are derived from
two different models. Model I is based on simple slab method of analysis. Model II is based on a modified
slab method in which the effect of shear stress in calculating the rolling force and torque is taken into
account. It is shown that the developed inverse analysis technique is reliable and can simultaneously
determine a more accurate flow stress for the material as well as a better estimation for the interface friction
factors.
Bookmarks Related papers MentionsView impact