• Nuclear Engineering and Technology
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• 제52권9호
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• pp.2100-2106
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• 2020

This paper newly proposes an efficient analytic springback prediction method to predict the final dimensions of bent cylindrical tubes for a helical tube steam generator in a small modular reactor. Three-dimensional bending procedure is treated as a two-dimensional in-plane bending procedure by integrating the Euler beam theory. To enhance the accuracy of the springback prediction, mathematical representations of flow stress and elastic modulus for unloading are systematically integrated into the analytic prediction model. This technique not only precisely predicts the final dimensions of the bent helical tube after a springback, but also effectively predicts the various target radii. Numerical validations were performed for five different radii of helical tube bending by comparing the final radius after a springback.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2007년도 춘계학술대회 논문집
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• pp.285-288
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• 2007

The use of commercial finite element analysis software to perform the entire process analysis and springback analysis has increased fast for last decade. Pamstamp2G is one of commercial software to be used widely in the world but it has still not been perfected in the springback prediction accuracy. We must select the combination of input parameters for the highest springback prediction accuracy in Pamstamp2G because springback prediction accuracy is sensitive to input parameters. Then we study the affect of input parameters to use member part for acquiring high springback prediction accuracy in Pamstamp2G. First, we choose important four parameters which are adaptive mesh level at drawing stage and cam flange stage, Gauss integration point number through the thickness and cam offset on basis of experiment. Second, we make a orthogonal array table L82[(7)] which is consist of 8 cases to be combined 4 input parameters, compare to tryout result and select main factors after analyzing affect factors of input parameters by Taguchi's method in 6 sigma. Third, we simulate after changing more detail the conditions of parameters to have big affect. At last, we find the best combination of input parameters for the highest springback prediction accuracy in Pamstamp2G. The results of the study provide the selection of input parameters to Pamstamp2G users who want to Increase the springback prediction accuracy.

• 소성∙가공
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• 제23권6호
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• pp.380-385
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• 2014

An analysis procedure is proposed to improve the prediction accuracy of springback as well as to evaluate the structural stability of the tooling used for fabricating a side sill part from UHSS. The analysis couples the stamping analysis and the subsequent analysis of the tool structural. The deformation and stress results for the tool structure are obtained from the proposed analysis procedure. The results show that the amount of deformation and stresses are so high that the tool structure must be reinforced and the tooling design must consider structural stability. Springback is predicted with CAE in order to compare the prediction accuracy between the given tool geometry and the geometry from the structural analysis. The simulation results with the deformed tool can predict the experimental springback tendency accurately.

• 소성∙가공
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• 제25권6호
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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.

• 소성∙가공
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• 제13권6호
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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.

• 소성∙가공
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• 제29권5호
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• pp.241-250
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• 2020

In this study, the methodology of the springback prediction of automotive parts applied 3rd generation AHSS was investigated using the response surface model analysis based on a regression model, and the meta model analysis based on a Kriging model. To design the learning data set for constructing the springback prediction models, and the experimental design was conducted at three levels for each processing variable using the definitive screening designs method. The hat-shaped member, which is the basic shape of the member parts, was selected and the springback values were measured for each processing type and processing variable using the finite element analysis. When the nonlinearity of the variables is small during the hat-shaped member forming, the response surface model and the meta model can provide the same processing parameter. However, the accuracy of the springback prediction of the meta model is better than the response surface model. Even in the case of the simple shape parts forming, the springback prediction accuracy of the meta model is better than that of the response surface model, when more variables are considered and the nonlinearity effect of the variables is large. The efficient global optimization algorithm-based Kriging is appropriate in resolving the high computational complexity optimization problems such as developing automotive parts.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2007년도 추계학술대회 논문집
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• pp.67-69
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• 2007

Unusual mechanical constitutive behavior of magnesium alloy sheets has been implemented into the finite element program ABAQUS via user material subroutine. For the verification purpose, the springback of AZ31B magnesium alloy sheet was measured using the unconstrained cylindrical bending test of Numisheet'2002. In addition to the developed constitutive models, the other two models based on isotropic constitutive equations with tensile and compressive properties were also considered. Preliminary comparisons have been made between simulated results by the finite element analysis and corresponding experiments and the newly proposed model showed enhanced prediction capability in springback prediction.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 춘계학술대회 논문집
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• pp.47-52
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• 2004

Springback comes from the release of residual stress 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 predication before the die machining has been a long goal in the Held of sheet metal forming. The aim 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.

• Structural Engineering and Mechanics
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• 제56권3호
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• pp.369-383
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• 2015

In this study, effect of various material hardening models based on Holloman's isotropic, Ziegler's linear kinematic, non-linear kinematic and mixture of the isotropic and nonlinear kinematic hardening laws on springback prediction of titanium alloy (Ti-3Al-2.5V) in a tube rotary draw bending (RDB) process was investigated with presenting the keynotes for a comprehensive step by step ABAQUS simulation. Influence of mandrel on quality of the final product including springback, wall-thinning and cross-section deformation of the tube was investigated, too. Material parameters of the hardening models were obtained based on information of a uniaxial test. In particular, in the case of combined iso-nonlinear kinematic hardening the material constants were calibrated by a simple approach based on half-cycle data instead of several stabilized cycles ones. Moreover, effect of some material and geometrical parameters on springback was carried out. The results showed that using the various hardening laws separately cannot describe the material hardening behavior correctly. Therefore, it is concluded that combining the hardening laws is a good idea to have accurate springback prediction. Totally the results are useful for predicting and controlling springback and cross-section deformation in metal forming processes.

• Advances in nano research
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• 제17권2호
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• pp.109-124
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• 2024

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.