Sensitive factors in springback simulation for sheet metal forming

Xu, Wangqiong; Ma, Chunrong; Li, C.H.; Wang, Feng

doi:10.1016/j.jmatprotec.2004.04.044

Cited by 89 publications

(39 citation statements)

References 7 publications

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“…Many publications deal with the determination of the optimal number of integration points through the sheet thickness and the proposed number of integrations points varies from 5 to 51. In the case of non-linear analysis, five integration points are sufficient to provide accurate results [24], while Xu et al [25] concluded that usually seven integration points are sufficient. On the contrary, Wagoner and Li [26] found that to analyse the springback with 1% computational error, up to 51 points are required for shell type elements.…”

Section: Introductionmentioning

confidence: 99%

Effect of Computational Parameters on Springback Prediction by Numerical Simulation

Trzepieciński

Lemu

2017

Metals

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Elastic recovery of the material, called springback, is one of the problems in sheet metal forming of drawpieces, especially with a complex shape. The springback can be influenced by various technological, geometrical, and material parameters. In this paper the results of experimental testing and numerical study are presented. The experiments are conducted on DC04 steel sheets, commonly used in the automotive industry. The numerical analysis of V-die air bending tests is carried out with the finite element method (FEM)-based ABAQUS/Standard 2016 program. A quadratic Hill anisotropic yield criterion is compared with an isotropic material described by the von Mises yield criterion. The effect of a number of integration points and integration rules on the springback amount and computation time is also considered. Two integration rules available in ABAQUS: the Gauss' integration rule and Simpson's integration rule are considered. The effect of sample orientation according to the sheet rolling direction and friction contact behaviour on the prediction of springback is also analysed. It is observed that the width of the sample bend in the V-bending test influences the stress-state in the cross-section of the sample. Different stress-states in the sample bend of the V-shaped die cause that the sheet undergoes springback in different planes. Friction contact phenomena slightly influences the springback behaviour.

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Section: Introductionmentioning

confidence: 99%

Effect of Computational Parameters on Springback Prediction by Numerical Simulation

Trzepieciński

Lemu

2017

Metals

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“…Others found five or seven integration point layers sufficient, cf. Bjørkhaug and Welo (2004), and Xu et al (2004).…”

Section: Forming Simulationmentioning

confidence: 99%

Finite Element Analysis of Sheet Metal Assemblies: Prediction of Product Performance Considering the Manufacturing Process

Govik¹

2014

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“…Springback prediction by FEA is a complicated task [3] because its accuracy strongly depends on physical [4] and numerical model assumptions [5], such as the material model [6], and because of its great sensitivity to numerical as well as physical parameters [7].…”

Section: Simulation Of Springbackmentioning

confidence: 99%

Strategies for springback compensation regarding process robustness

Gösling

Kracker

Brosius

et al. 2010

Prod. Eng. Res. Devel.

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In this article, strategies which compensate geometrical deviations caused by springback are discussed using finite element simulations and statistical modelling techniques. First of all the ability to predict springback using a finite element simulation model is analysed. For that purpose numerical predictions and experiments are compared with each other regarding the amount of springback. In a next step, different strategies for compensating springback such as a modification of stress condition, component stiffness and tool geometry are introduced. On the basis of finite element simulations these different compensation strategies are illustrated for a stretch bending process and experimentally checked for an example. Finally springback simulations are compared regarding their robustness against noise variables such as friction and material properties. Thereby a method based on statistical prediction models is introduced which allows for an accurate approximation of the springback distribution with less numerical effort in comparison to a classical Monte-Carlo method.

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Sensitive factors in springback simulation for sheet metal forming

Cited by 89 publications

References 7 publications

Effect of Computational Parameters on Springback Prediction by Numerical Simulation

Effect of Computational Parameters on Springback Prediction by Numerical Simulation

Finite Element Analysis of Sheet Metal Assemblies: Prediction of Product Performance Considering the Manufacturing Process

Strategies for springback compensation regarding process robustness

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