Development of Electrohydraulic Forming Process for Aluminum Sheet with Sharp Edge

Shim, Ji-Yeon; Kang, Bong-Yong

doi:10.1155/2017/2715092

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…The expansion of the plasma channel in the surrounding medium is due mainly to the rapid and pronounced pressure increase in the spark channel between the electrodes, caused by the temperature rise during the rapid and high energy input [9]. This process takes place in a time interval of less than 100 µs in most cases [11,12]. The shock wave caused by the expansion of the plasma channel is transmitted to the surrounding medium as a pressure wave and then propagates in this at the speed of sound, which is 1500 m/s in water [7].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Electrohydraulic and Electromagnetic Sheet Metal Forming against the Background of the Application as an Incremental Processing Technology

Heggemann

Psyk

Oesterwinter

et al. 2022

Metals

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High-speed forming processes such as electromagnetic forming (EMF) and electrohydraulic forming (EHF) have a high potential for producing lightweight components with complex geometries, but the forming zone is usually limited to a small size for equipment-related reasons. Incremental strategies overcome this limit by using a sequence of local deformations to form larger component areas gradually. Hence, the technological potential of high-speed forming can be exploited for large-area components too. The target-oriented process design of such incremental forming operations requires a deep understanding of the underlying electromagnetic and electrohydraulic forming processes. This article therefore analyzes and compares the influence of fundamental process parameters on the acting loads, the resulting course of deformation, and the forming result for both technologies via experimental and numerical investigations. Specifically, it is shown that for the EHF process considered, the electrode distance and the discharge energy have a significant influence on the resulting forming depth. In the EHF process, the largest forming depth is achieved directly below the electrodes, while the pressure distribution in the EMF depends on the fieldshaper used. The energy requirement for the EHF process is comparatively low, while significantly higher forming speeds are achieved with the EMF process.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Electrohydraulic and Electromagnetic Sheet Metal Forming against the Background of the Application as an Incremental Processing Technology

Heggemann

Psyk

Oesterwinter

et al. 2022

Metals

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“…When a sheet metal is deformed under highspeed conditions, the formability is improved by the diesheet interaction and the inertia effect of the sheet. Many researchers have demonstrated the improvement of the formability through various experiments and finite element analyses [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Numerical Estimation of Material Properties in the Electrohydraulic Forming Process Based on a Kriging Surrogate Model

Woo

Lee

Song

et al. 2020

Mathematical Problems in Engineering

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High-speed forming processes, such as electrohydraulic forming, have recently attracted attention with the development of forming technology. However, because the high-speed operation (above 100 m/s) raises safety concerns, most experiments are conducted in a closed die, which hides the forming process. Therefore, the experimental process can only be observed in a numerical simulation with accurate material properties. The conventional quasistatic material properties are improper for high-speed forming simulations with high strain rates (>102 s−1). In this study, the material properties of Al 6061-T6, which reflect the deformation behavior in the high-strain-rate region, were investigated in a numerical approach based on a reduced order model and a surrogate model in which the numerical results resemble the experimental results. The strain rate effect on the material was determined by the Cowper–Symonds constitutive equation, and two strain rate parameters were predicted. The surrogate model takes two material parameters as inputs and outputs a weighting coefficient calculated by the reduced order model. The surrogate model is based on the Kriging method to reduce the simulation cost. Next, the optimal material parameters that minimize the error between the surrogate model and the experiments are estimated by nonlinear least-squares optimization using a genetic algorithm and the constructed surrogate model. The predicted optimal parameters were verified by comparing the results of the experiment, numerical simulation, and surrogate model.

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“…Researchers have found that the high-velocity forming process can improve the formability of a sheet metal having low formability, such as magnesium alloys, titanium alloys, and some aluminum alloys [1]. Shim and Kang [2] used the 5000 series of aluminum alloys for the EHF experiment and showed formability improvement in the EHF process by comparing Forming limit diagrams (FLDs) in quasi-static and high-speed conditions. Gillard et al [3] and Golovashchenko et al [4,5] showed that the formability of the DP 980 sheet was improved by die–sheet interactions in the EHF experiment using dies of various shapes.…”

Section: Introductionmentioning

confidence: 99%

Acquisition of Dynamic Material Properties in the Electrohydraulic Forming Process Using Artificial Neural Network

et al. 2019

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Electrohydraulic forming is a high-velocity forming process that deforms sheet metals with velocities above 100 m/s and strain rates more than 100 s−1. This experiment was conducted in a closed space because of safety concerns related to the high-velocity conditions; therefore, we were not able to examine the deformation process of the sheet metal. To observe the electrohydraulic forming process in detail, we performed virtual numerical simulations using accurate material properties. Therefore, in this paper, we obtained the material property of a sheet metal from a numerical estimation by using a surrogate model based on the reduced order model and the artificial neural network. The Cowper–Symonds constitutive equation was selected for the Al 6061-T6 sheet metal, and two strain rate parameters were adopted as the unknown parameters. From the two sampling techniques, the training and test samples were extracted from the specific ranges of two unknown parameters, and a numerical simulation was performed for these samples by using the LS-DYNA program. The z-axis displacements of the deformed sheet metal were obtained from the results of the numerical simulation, and two basis vectors were extracted by using principal component analysis. In addition, to predict the weighting coefficients of the two basis vectors at the defined range of parameters, we used the artificial neural network technique as a surrogate model. By comparing the surrogate model and the experimental results and calculating the root mean square error value, we estimated the optimal parameter for Al 6061-T6. Finally, the reliability of the obtained material parameters was proved by comparing the experimental results, the surrogate model, and LS-DYNA.

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Development of Electrohydraulic Forming Process for Aluminum Sheet with Sharp Edge

Cited by 11 publications

References 12 publications

Comparative Analysis of Electrohydraulic and Electromagnetic Sheet Metal Forming against the Background of the Application as an Incremental Processing Technology

Comparative Analysis of Electrohydraulic and Electromagnetic Sheet Metal Forming against the Background of the Application as an Incremental Processing Technology

Numerical Estimation of Material Properties in the Electrohydraulic Forming Process Based on a Kriging Surrogate Model

Acquisition of Dynamic Material Properties in the Electrohydraulic Forming Process Using Artificial Neural Network

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