• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.180-183
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• 2003

In this research, on-line model for prediction of effective strain distribution hi strip on finishing mill process is prescribed. It has been developed using several selected non-dimensional parameters and previously made average effective strain model via series of finite element process simulations, $\Delta$$\varepsilon$ was introduced to describe the effective strain distribution in strip. To confirm adequate non-dimensional variables uniqueness test was done. And to decide the order of polynomial in on-line model equation tendency test for each variables was done. The prediction accuracy of the proposed model is examined through comparison with finite element calculation results.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.10a
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• pp.96-99
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• 2007

The microstructure with fine and uniform AGS(austenite grain size) along thickness direction over no recrystallization temperature is strongly required for production of the high strength steels. The previous AGS prediction only based on the average strain improves to find the rolling conditions for accomplishment of the fine grain, but cannot find those for uniform grain. In this paper, an integrated mathematical model for prediction of the strain distribution along thickness direction is developed by carrying out finite element simulation for a series of rolling conditions. Also, the AGS distribution after rough rolling is predicted by applying the proposed model with AGS prediction model.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.359-367
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• 2004

In this research on-line model for the prediction of the effective strain distribution in strip on finishing mill process is presented. To describe the effective strain distribution in strip, three guide points and a distribution fitting variable are used. On-line models to get these points and fitting variable non-dimensionalization method and least square method were used with FEM simulation results. The model is developed using strip only FEM simulation as reference sets and compared with roll coupled FEM simulation results as perturbed sets. The on-line model to describe effective strain distribution shows good agreement with coupled FEM analysis results.

• Proceedings of the Korean Reliability Society Conference
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• 2000.04a
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• pp.213-222
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• 2000

In present study, a stochastic model is developed for the low cycle fatigue life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. In the proposed model, fatigue phenomenon is considered as a Markov process, and damage vector and reliability are defined on every plane. Any low cycle fatigue damage evaluating method can be included in the proposed model. The model enables calculation of statistical reliability and crack initiation direction under variable multiaxial loading, which are generally not available. In present study, a critical plane method proposed by Kandil et al., maximum tensile strain range, and von Mises equivalent strain range are used to calculate fatigue damage. When the critical plane method is chosen, the effect of multiple critical planes is also included in the proposed model. Maximum tensile strain and von Mises strain methods are used for the demonstration of the generality of the proposed model. The material properties and the stochastic model parameters are obtained from uniaxial tests only. The stochastic model made of the parameters obtained from the uniaxial tests is applied to the life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. The predicted results show good accordance with experimental results.

• Journal of Power System Engineering
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• v.21 no.1
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• pp.43-49
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• 2017

This work aims to investigate on the low cycle fatigue life assessment, which is adopted on the strain-life relationship, or better known as the Coffin-Manson relationship, and also the strain energy density-based model. The low cycle fatigue test results of Alloy 617 weldments under $900^{\circ}C$ have been statistically estimated through the Coffin-Manson relationship according to the provided strain profile. In addition, the strain energy density-based model is proposed to represent the energy dissipated per cycle as fatigue damage parameter. Based on the results, Alloy 617 weldments followed the Coffin-Manson relationship and strain energy density-based model well, and they were compatible with the experimental data. The predicted lives based on these two proposed models were examined with the experimental data to select a proper life prediction parameter.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.10a
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• pp.259-262
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• 2009

The influence of pre-strain in low-cycle fatigue behavior of Fe-18Mn-0.05Al-0.6C TWIP steel was studied by conducting axial strain-controlled tests. As-received plates were deformed by rolling with reduction ratios of 10 and 30%, respectively. A triangular waveform with a constant frequency of 1 Hz was employed for low cycle fatigue test at the strain amplitudes in the range of ${\pm}0.4{\sim}{\pm}0.6$ pct. The results showed that low-cycle fatigue life was strongly dependent on the amount of pre-strain as well as the strain amplitude. Increasing the amount of prestrain, the number of reversals to failure was significantly decreased at high strain amplitudes, but the effect was negilgible at low strain amplitudes. A new model for predicting fatigue life of pre-strained body has been devised adding a correction term of ${\Delta}E_{pre-strain}$ to the energy-based fatigue damage parameter.

• Tunnel and Underground Space
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• v.28 no.3
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• pp.277-291
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• 2018

LSTM (Long Short-Term Memory) algorithm which is a kind of recurrent neural network was used to establish a model to predict the stress-strain curve of an material under uniaxial compression. The model was established from the stress-strain data from uniaxial compression tests of silica-gypsum specimens. After training the model, it can predict the behavior of the material up to the failure state by using an early stage of stress-strain curve whose stress is very low. Because the LSTM neural network predict a value by using the previous state of data and proceed forward step by step, a higher error was found at the prediction of higher stress state due to the accumulation of error. However, this model generally predict the stress-strain curve with high accuracy. The accuracy of both LSTM and tangential prediction models increased with increased length of input data, while a difference in performance between them decreased as the amount of input data increased. LSTM model showed relatively superior performance to the tangential prediction when only few input data was given, which enhanced the necessity for application of the model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.100-105
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• 2019

In wire drawing, aterial deformation is concentrated on the surface of the drawn wire because of surface contact with the drawing die. Therefore, strain varies from the center to the surface of the drawn wire. In this study, based on the upper bound method, an effective strain prediction method from the center to the surface of a drawn wire was proposed. Using the proposed method, the effective strain of the drawn wire was calculated verify the proposed prediction method, the predicted effective strain was compared with the result of finite element analysis.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.805-814
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• 2008

This study focused on the resilient modulus prediction model, which is the functions of mean effective principal stress and axial strain, for three types of railroad trackbed materials such as crushed stone, weathered soil, and crushed-rock soil mixture. The model is composed with the maximum Young's modulus and nonlinear values for higher strain in parallel with dynamic shear modulus. The maximum values is modeled by model parameters, $A_E$ and the power of mean effective principal stress, $n_E$. The nonlinear portion is represented by modified hyperbolic model, with the model parameters of reference strain, ${\varepsilon}_r$ and curvature coefficient, a. To assess the performance of the prediction models proposed herein, the elastic response of a test trackbed near PyeongTaek, Korea was evaluated using a 3-D nonlinear elastic computer program (GEOTRACK) and compared with measured elastic vertical displacement during the passages of freight and passenger trains. The material types of sub-ballasts are crushed stone and weathered granite soil, respectively. The calculated vertical displacements within the sub-ballasts are within the order of 0.6mm, and agree well with measured values with the reasonable margin. The prediction models are thus concluded to work properly in the preliminary investigation.

• Computers and Concrete
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• v.30 no.6
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• pp.377-391
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• 2022

The use of carbon fiber-reinforced polymers (CFRP) has widely increased due to its enhancement in the ultimate strength and ductility of the reinforced concrete (RC) structures. This study presents a prediction model for the axial compressive strength and strain of normal-strength concrete cylinders confined with CFRP. Besides, soft computing approaches have been extensively used to model in many areas of civil engineering applications. Therefore, the genetic expression programming (GEP) models to predict axial compressive strength and strain of CFRP-confined concrete specimens were used in this study. For this purpose, the parameters of 283 CFRP-confined concrete specimens collected from 38 experimental studies in the literature were taken into account as input variables to predict GEP based models. Then, the results of GEP models were statistically compared with those of models proposed by various researchers. The values of R² for strength and strain of CFRP-confined concrete were obtained as 0.897 and 0.713, respectively. The results of the comparison reveal that the proposed GEP-based models for CFRP-confined concrete have the best efficiency among the existing models and provide the best performance.