• Transactions of Materials Processing
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• v.4 no.3
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• pp.214-221
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• 1995

In the paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in the neural network. The architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of a neural network, an optimal billet is determined by applying the nonlinear mathematical relationship between the aspect ratios in the initial billet and the final products. The amount of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet aspect ratios and those of the unfilled volumes. After learning, the system is able to predict the filling regions which are exactly the same or slightly different to the results of finite element simulation. This new method is applied to find the optimal billet size for the plane strain rib-web product in cold forging. This would reduce the number of finite element simulation for determining the optimal billet size of forging product, further it is usefully adapted to physical modeling for the forging design.

• Transactions of Materials Processing
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• v.27 no.1
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• pp.28-36
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• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

• Transactions of Materials Processing
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• v.27 no.4
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• pp.227-235
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• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• Journal of Korean Biological Nursing Science
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• v.18 no.4
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• pp.274-279
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• 2016

Purpose: Adolescents who experienced the alcohol consumption have gradually increased. Adolescence is a critical period of the neural plasticity in the brain. Neural plasticity is mediated by neurotrophins and has an impact on cognitive function. Environmental enrichment ameliorates the cognitive function and increases neurotrophins. Thus, we investigated the neuroprotective effect of environmental enrichment on ethanol induced cognitive impairment in adolescent rats. Methods: The ethanol groups and the controls groups were injected with ethanol (0.5g/kg) and phosphate buffered saline, respectively, through intraperitoneal from 28th day of birth for 11 days. The environmental enrichment groups were provided larger cages containing toys than the standard cage. Passive avoidance test and Y-maze test were performed to evaluate the spatial memory. Results: Environmental enrichment+ethanol group showed higher alterations than the standard environment+ethanol group in Y-maze test (p<.05). In hippocampus, The environmental enrichment+ethanol group showed significantly higher level of the number of c-fos positive celsl and density of tropomyosin receptors kinase B receptor than the standard environment+ethanol group (p<.05). Conclusion: So, we suggested that the environmental enrichment played a role as a prophylaxis for prevention of memory impairment induced by ethanol exposure in adolescence.

• Transactions of Materials Processing
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• v.8 no.4
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• pp.331-339
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• 1999

In this study, to analyze the shaped drawing process from round bar, the practical conical die with considering die radius and bearing was defined by a mathematical expression, and also a simple technique for initial mesh generation to the shaped drawing process was proposed. The drawing of rectangular section from round bar, one of the shaped drawing process, has been simulated by using non-steady state 3D rigid plastic finite element method in order to evaluate the influence of semi-die angle and reduction in area to corner filling. Other process variables such as friction constant, rectangular ratio, die radius and bearing length were fixed during the simulation. An artificial neural network has been introduced to obtain the optimal process conditions which gave rise to a fast simulation.

• Transactions of Materials Processing
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• v.27 no.2
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• pp.100-106
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• 2018

In the continuous casting process, the incidence of transverse surface cracks on the piece may occur by multiple and diverse variables. It is noted that mathematical models may predict only the occurance of the transverse surface cracks, but can require a lot of time (more than three days) to produce a result with this process. This study applied neural networks to predict whether the cracks on the piece surface occurs or does not occur. The computation time was shortened to three minutes, making it applicable to an on-line program, which predicts the non-cracks or cracks of the piece surface in the actual continuous casting process. In addition, the operating conditions to prevent the occurrence of the transverse surface cracks, using decision boundaries were also suggested.

• Transactions of Materials Processing
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• v.31 no.2
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• pp.81-88
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• 2022

Numerical analysis and dynamic material properties are required to analyze the behavior of workpiece during an electrohydraulic forming (EHF) process. In this study, EHF experiments were conducted under three conditions (6, 7, 8 kV). Dynamic material properties of Al 5052-H34 were inversely estimated through an ANN (Artificial Neural Network) model constructed based on LS-Dyna analysis results. Parameters of Cowper-Symonds constitutive equation, C and p, were used to implement dynamic material properties. By comparing experimental results of three conditions with ANN model results, optimized parameters were obtained. To determine the reliability of the derived parameters, experimental results, LS-Dyna analysis results, and ANN results of three conditions were compared using MSE and SMAPE. Valid parameters were obtained because values of indicators were within confidence intervals.

• Journal of Korean Neurosurgical Society
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• v.46 no.1
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• pp.1-4
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• 2009

Objective: To report an unsuspected adaptive plasticity of single upper motor neurons and of primary motor cortex found after microsurgical connection of the spinal cord with peripheral nerve via grafts in paraplegics and focussed discussion of the reviewed literature. Methods: The research aimed at making paraplegics walk again, after 20 years of experimental surgery in animals. Amongst other things, animal experiments demonstrated the alteration of the motor endplates receptors from cholinergic to glutamatergic induced by connection with upper motor neurons. The same paradigm was successfully performed in paraplegic humans. The nerve grafts were put into the ventral-lateral spinal tract randomly, with out possibility of choosing the axons coming from different areas of the motor cortex. Results: The patient became able to selectively activate the re-innervated muscles she wanted without concurrent activities of other muscles connected with the same cortical areas. Conclusion: Authors believe that unlike in nerve or tendon transfers, where the whole cortical area corresponding to the transfer changes its function a phenomenon that we call "brain plasticity by areas". in our paradigm due to the direct connection of upper motor neurons with different peripheral nerves and muscles via nerve grafts motor learning occurs based on adaptive neuronal plasticity so that simultaneous contractions of other muscles are prevented. We propose to call it adaptive functional "plasticity by single neurons". We speculate that this phenomenon is due to the simultaneous activation of neurons spread in different cortical areas for a given specific movement, whilst the other neurons of the same areas connected with peripheral nerves of different muscles are not activated at the same time. Why different neurons of the same area fire at different times according to different voluntary demands remains to be discovered. We are committed to solve this enigma hereafter.

• Journal of the Korean Geosynthetics Society
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• v.17 no.4
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• pp.199-212
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• 2018

This research concerns the prediction method for ground movement and wall member force due to determination structural stability check and failure check during deep excavation construction. First, research related with excavation influence parameters is conducted. Then, numerical analysis for various excavation conditions were conducted using Finite Element Method and Beam-column elasto-plasticity method. Excavation analysis database was then constructed. Using this database, development of ANN (artificial neural network) was performed for each ground movements and using structural member forces. By comparing the numerical analysis results with ANN's prediction, it is validated that development of ANN can be used efficient for prediction of ground movement and structural member forces in deep excavation site.

• Transactions of Materials Processing
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• v.22 no.5
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• pp.250-257
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• 2013

A series of finite element analyses of the rolling process were performed and a neural network algorithm was employed to calculate the amount of ski-effect for an arbitrary thickness of incoming material in the roll gap. Pilot hot plate rolling tests were also conducted to verify the usefulness of the finite element analyzes conducted in this study. In these experiments, plates with thicknesses varying from 25 to 65 mm were tested. In addition, a number of rolling reductions of up to 31% were examined. Finally, a number of circumferential upper and lower rolls were investigated. Experimental validations demonstrated that the neural network algorithm predicted the proper amount of ski when rolling conditions(material thickness, reduction ratio, roll velocity differential) changed arbitrarily.