• Transactions of Materials Processing
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• v.25 no.6
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• pp.396-401
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• 2016

Recently, Multi-strength hot stamping process has been widely used to achieve lightweight and crashworthiness in automotive industry. In concept of multi-strength hot stamping process, process design of tailor rolled blank(TRB) in partial heating is difficult because of thickness and temperature variation of blank. In this study, springback prediction of TRB in partial heating process was performed considering its thickness and temperature variation. In partial heating process, TRB was heated up to $900^{\circ}C$ for thicker side and below $Ac_3$ transformation temperature for thinner side, respectively. Johnson-Mehl-Avrami-Kolmogorov(JMAK) equation was applied to calculate austenite fraction according to heating temperature. Calculated austenite fraction was applied to FE-simulation for the prediction of springback. Experiment for partial heating process of TRB was also performed to verify prediction accuracy of FE-simulation coupled with JMAK equation.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.175-183
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• 2004

In hot strip rolling, a capability for precisely predicting roll force is crucial for sound process control. In the past, on-line prediction models have been developed mostly on the basis of Orowan's theory and its variation. However, the range of process conditions in which desired prediction accuracy could be achieved was rather limited, mainly due to many simplifying assumptions inherent to Orowan's theory. As far as the prediction accuracy is concerned, a rigorously formulated finite element(FE) process model is perhaps the best choice. However, a FE process model in general requires a large CPU time, rendering itself inadequate for on-line purpose. In this report, we present a FE-based on-line prediction model applicable to precision process control in a finishing mill(FM). Described was an integrated FE process model capable of revealing the detailed aspects of the thermo-mechanical behavior of the roll-strip system. Using the FE process model, a series of process simulation was conducted to investigate the effect of diverse process variables on some selected non-dimensional parameters characterizing the thermo-mechanical behavior of the strip. Then, it was shown that an on-line model for the prediction of roll force could be derived on the basis of these parameters. The prediction accuracy of the proposed model was examined through comparison with measurements from the hot strip mill.

• Transactions of Materials Processing
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• v.13 no.6 s.70
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• pp.490-496
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• 2004

Springback comes from the release of external loads after forming. The control of phenomenon is especially important in the sheet metal forming since there are no other practical methods available to correct the dimensional inaccuracy from springback. Therefore the accurate prediction before the die machining has been a long goal in the field of sheet metal forming. The am of the present study is to enhance the prediction capability of finite element (FE) analysis for the springback phenomenon. For this purpose FE analysis for V-bending has been carried out with the commercial programs, LS-DYNA. The FE analysis results have been validated through the comparison of experimental. The experimental results measured directly by the strain gauge have given the confidence to FEA.

• Korean Journal of Materials Research
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• v.34 no.7
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• pp.363-369
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• 2024

In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module 'Matminer', 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the 'Fe' compositional ratio. The feature importance method provided by 'scikit-learn' was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.301-310
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• 2022

Accurate prediction of mixed ground conditions ahead of a tunnel face is of vital importance for safe excavation using tunnel boring machines (TBMs). Previous studies have primarily focused on electrical resistivity surveys from the ground surface for geotechnical investigation. In this study, an FE (finite element) numerical model was developed to simulate electrical resistivity surveys for the prediction of risky mixed ground conditions in front of a tunnel face. The proposed FE model is validated by comparing with the apparent electrical resistivity values obtained from the analytical solution corresponding to a vertical fault on the ground surface (i.e., a simplified model). A series of parametric studies was performed with the FE model to analyze the effect of geological and sensor geometric conditions on the electrical resistivity survey. The parametric study revealed that the interface slope between two different ground formations affects the electrical resistivity measurements during TBM excavation. In addition, a large difference in electrical resistivity between two different ground formations represented the dramatic effect of the mixed ground conditions on the electrical resistivity values. The parametric studies of the electrode array showed that the proper selection of the electrode spacing and the location of the electrode array on the tunnel face of TBM is very important. Thus, it is concluded that the developed FE numerical model can successfully predict the presence of a mixed ground zone, which enables optimal management of potential risks.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.273-278
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• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

• Transactions of Materials Processing
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• v.28 no.2
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• pp.83-88
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• 2019

This paper introduces a new concept - on-line FE model, as applied to metal rolling. The new technology allows for completion of process simulation within a tiny fraction of a second without loss of high-level prediction accuracy inherent to FEM. The three steps of an on-line FE model design namely, process metamorphosis, mesh design, and process variable design, are described in detail. The procedure is demonstrated step by step through designing actual on-line models for the prediction of the dog-bone profile in edge rolling. The validity and prediction accuracy of the on-line FE models are analyzed and discussed.

• Transactions of Materials Processing
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• v.26 no.3
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• pp.162-167
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• 2017

The tensile surface residual stress in the multi-pass drawn wire deteriorates the mechanical properties of the wire. Therefore, the evaluation of the residual stress is very important. Especially, the axial residual stress on the wire surface is the highest. Therefore, the objective of this study was to propose an axial surface residual stress prediction model of the multi-pass drawn steel wire. In order to achieve this objective, an elastoplastic finite element (FE) analysis was carried out to investigate the effect of semi-die angle and reduction ratio of the axial surface residual stress. By using the results of the FE analysis, a surface residual stress prediction model was proposed. In order to verify the effectiveness of the prediction model, the predicted residual stress was compared to that of a wire drawing experiment.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10b
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• pp.26-30
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• 2003

Since the dimension of cold forged part is larger than the cavity size of forging die, the difference results from the various features, such as, the elastic characteristics of die and workpiece, thermal influences, and machine-elasticity. All of these factors should be considered to get more accurate prediction of the dimension of forged part. In this paper, severe FE techniques are proposed to improve the prediction accuracy of dimension for cold forged part. To validate the importance of the above mentioned factors, and the estimated results are compared with the experimental results. The used model is a closed die upsetting of cylindrical billet. The calculated dimensions are well coincided with .the measured values based on the proposed techniques. The proposed techniques have put two simple but important points into Fe simulation. One is the separation of forging stages into 3 steps, from a loading through punch retraction to ejecting stage. The other is the dimensional change, according to the temperature changes due to the deformation. The FE analysis could predict the dimension of cold forged part within the $10{\mu}m$, based on the more realistic consideration.

• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.261-274
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• 2014

An accurate finite element (FE) model of a structure is essential for predicting reliably its dynamic characteristics. Such a model is used to predict the effects of structural modifications for dynamic design of the structure. These modifications may be imposed by design alterations for operating reasons. Most of the model updating techniques neglect damping and so these updated models can't be used for accurate prediction of vibration amplitudes. This paper deals with the basic formulation of damped finite element model updating method and its use for structural dynamic modifications. In this damped damped finite element model updating method, damping matrices are updated along with mass and stiffness matrices. The damping matrices are updated by updating the damping coefficients. A case involving actual measured data for the case of F-shaped test structure, which resembles the skeleton of a drilling machine is used to evaluate the effectiveness of damped FE model updating method for accurate prediction of the vibration levels and thus its use for structural dynamic modifications. It can be concluded from the study that damped updated FE model updating can be used for structural dynamic modifications with confidence.