• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.08a
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• pp.341-348
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• 1999

In the finite elemenet analysis of metal forming problems, the most critical input is the flow stress of workpiece. Conventionally, the flow stress of a metal at elevated temperatures is assumed to be a function of strain, strain rate and temperature, and obtained by experiment. However, if the workpiece is not continuously deformed as in mulit-pass rolling, the flow stress obtained by experiment is no longer valid because it does not consider the microstructure evolution occurring between deformations. In the present study, it was attemped that the flow stress of steel in the austenite region be obtained equations. It was applied to the prediction of flow stress variation at each stand during hot finishing rolling of steel.

• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.889-894
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• 2001

In this study, the performances of various turbulence closure models are evaluated in the calculation of a transonic flow over axisymmetric bump. k-$\varepsilon$, explicit algebraic stress, and two Reynolds stress models, i.e., GL model proposed by Gibson & Launder and SSG model proposed by Speziale, Sarkar and Gatski, are chosen as turbulence closure models. SSG Reynolds stress model gives best predictions for pressure coefficients and the location of shock. The results with GL model also show quite accurate prediction of pressure coefficients down-stream of shock wave. However, in the predictions of mean velocities and turbulent stresses, the results are not so satisfactory as in the prediction of pressure coefficients.

• 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.

• Transactions of Materials Processing
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• v.9 no.1
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• pp.72-79
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• 2000

Dynamic recrystallization (DRX), which may occur during hot deformation, is important for the microsturctural evolution of 304 stainless steel. Especially, the current interest in modelling hot rolling demands quantitative relationships among the thermomechanical process variables, such as strain, temperature, strain rate, and etc. Thus, this paper individually presents the relationships for flow stress and volume fraction of DRX as a function of processing variables using torsion tests. The hot torsion tests of 304 stainless steel were performed at the temperature range of 900~110$0^{\circ}C$ and the strain rate range of 5x10-2~5s-1 to study the high temperature softening behavior. For the exact prediction of flow stress, the equation was divided into two regions, the work hardening (WH) and dynamic recovery (DRV) region and the DRX region. Especially, The flow stress of DRX region could be expressed by using the volume fraction of DRX (XDRX). Since XDRX was consisted of the critical strain($\varepsilon$c) for initiation of dynamic recrystallization (DRX) and the strain for maximum softening rate ($\varepsilon$*), that were related with the evolution of microstructure. The calculated results predicted the flow stress and the microstructure of the alloy at any deformation conditions well.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.427-733
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• 2003

Adiabatic shear bands have been observed in the serrated chip during high strain rate metal cutting process of medium carbon steel and titanium alloy. The recent microscopic observations have shown that dynamic recrystallization occurs in the narrow adiabatic shear bands. However the conventional flow stress models such as the Zerilli-Armstrong model and the Johnson-Cook model, in general, do not predict the occurrence of dynamic recrystallization (DRX) in the shear bands and the thermal softening effects accompanied by DRX. In the present study, a strain hardening and thermal softening model is proposed to predict the adiabatic shear localized chip formation. The finite element analysis (FEA) with this proposed flow stress model shows that the temperature of the shear band during cutting process rises above 0.5T$\sub$m/. The simulation shows that temperature rises to initiate dynamic recrystallization, dynamic recrystallization lowers the flow stress, and that adiabatic shear localized band and the serrated chip are formed. FEA is also used to predict and compare chip formations of two flow stress models in orthogonal metal cutting with AISI 1045. The predictions of the FEA agreed well with the experimental measurements.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.7
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• pp.2257-2265
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• 1996

In the cold rolling mill, it is very important that a constrained static flow stress of rolled strip and rolling force calculation model be exactly considered to improve an prediction accuracy for rolling forces. Therefore, in this study, the values of the constrained static flow stress are used by deriving the regression equation which is a function of rolling conditions(FDT, CT) and chemical compositions(C, Si, Mn), previously applied by making the tables of yield strength for hot coils with size. And with the consideration that an elastic deformation part of an rolled strip appears at the entry and delivery side of the contacting area between the work roll and rolled strip is calculated. By applying these methods, the more accurate prediction for rolling force is obtained. As a results, the deviation of thickness is significantly reduced in the rolling direction.

• The Journal of Korean Association of Computer Education
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• v.15 no.3
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• pp.61-69
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• 2012

The purpose of the present study is to examine the prediction of academic self-efficacy, learning flow, academic stress, and emotional exhaustion on course satisfaction of cyber university students. The total of 536 students registered in a meditation course at W cyber university was participated in the web-based survey in the spring semester of 2011, and finally 331 students completed this survey. The hypothetical model proposed was composed of academic self-efficacy, learning flow, academic stress, emotional exhaustion as the predictor variables, and course satisfaction as the criterion variable. According to the results of this study through multiple regression analysis, academic self-efficacy, learning flow, academic stress, and emotional exhaustion significantly predicted on course satisfaction. Based on the results of this study, effective methods and strategies for constructing cyber educational environments that enable students to improve academic self-efficacy and learning flow as well as reducing academic stress and emotional exhaustion should be considered.

• Journal of computational fluids engineering
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• v.6 no.4
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• pp.35-42
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• 2001

In this study, numerical calculations are carried out in order to evaluate the performance of low-Re Reynolds stress model based on SSG model for a swirling turbulent flow in a pipe. The results are compared with those of k-ε model, GL model and the experimental data. The results show that low-Re Reynolds stress model and GL model give better results than k-ε model. In the region near the wall, low-Re Reynolds stress model improves the predictions. However, there is no large difference between the predictions with two Reynolds stress models.

• International Journal of Fluid Machinery and Systems
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• v.6 no.2
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• pp.87-93
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• 2013

In order to study the effect of the fluid-structure interaction (FSI) on the simulation results, the external characteristics and internal flow features of a diffuser pump were analyzed with a two-way flow solid coupling method. And the static and dynamic structure analysis of the blade was also caculated with the FEA method. The steady flow field is based on Reynolds Averaged N-S equations with standard $k-{\varepsilon}$ turbulent model, the unsteady flow field is based on the large eddy simulation, and the structure response is based on elastic transient structural dynamic equation. The results showed that the effect of FSI on the head prediction based on CFD really exists. At the same radius, the van mises stress on the nodes closed shroud and hub was larger than other nodes. A large deformation region existed near inlet side at the middle of blades. The strength of impeller satisfied the strength requirement with static stress analysis based on the fourth strength theory. The dynamic stress varied periodically with the impeller rotating. It was also found that the fundamental frequency of the dynamic stress is the rotating frequency and its harmonic frequency. The frequency of maximum stress amplitude at node 1626 was 7 times of the rotating frequency. The frequency of maximum stress amplitude at node 2328 was 14 times of the rotating frequency. No matter strength failure or fatigue failure, the root of blades near shroud is the key region to analyse.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.7
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• pp.1030-1041
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• 1997

The control model in the tandem cold rolling mill consists of many mathematical theories and is used to calculate the reference values such as the roll gap and the rolling speed for good operation of rolling mill. But, the control model used presently has a problem causing inaccurate prediction of the rolling force. By the parameter identification, it was found that the main factor causing inaccurate prediction of the rolling force was incorrect modeling of the friction coefficient and the flow stress. To get rid of the erroneous factor new adaptive schemes are suggested in this work. Those are a long-time adaptation by the iterative least-square method and a short-time adaptation by the recursive weighted least-square method respectively. The new equations for the friction coefficient and the flow stress are derived by applying the suggested adaptive algorithms. Through the on-line test in an actual mill, it is proved that the rolling force predicted by the new equations is more accurate than the one by the existing equations ever used.