Simulation of springback variation in forming of advanced high strength steels

Chen, Peng; Koç, Muammer

doi:10.1016/j.jmatprotec.2007.02.046

Cited by 122 publications

(59 citation statements)

References 26 publications

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“…In this method stochastic flow curves are generated by scaling the nominal stress-strain curve, c.f. (Lönn et al 2009;Aspenberg et al 2013;Chen and Koç 2007;Del Prete et al 2010). The nominal stress-strain curve is generated using the power law hardening relation…”

Section: Methods 1amentioning

confidence: 99%

“…However, due to lack of experimental data these approximative modeling methods have also been used in later stages of the design process. For example in (Lönn et al 2009;Aspenberg et al 2013;Chen and Koç 2007;Del Prete et al 2010) variations in material properties are incorporated by simple scaling of the nominal stress-strain curve, where the scaling factor often is based on variation of either the yield stress, R p0.2 or the ultimate stress, R m . Another commonly used simplified approach is to incorporate material scatter by varying the parameters in an analytical material hardening relation (Ledoux et al 2007;Marretta and Di Lorenzo 2010;Jansson et al 2008;Müllerschön et al 2007;Li et al 2009;Quetting et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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An evaluation of simple techniques to model the variation in strain hardening behavior of steel

Shetty

Nilsson

2016

Struct Multidisc Optim

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It is important to consider variations in material parameters in the design of automotive structures in order to obtain a robust and reliable design. However, expensive tests are required to gain complete knowledge of the material behavior and its associated variation. Consequently, due to time and cost constraints, simplified material scatter modeling techniques based on scatter data of typical material properties provided by the material suppliers are used at early design stages in simulation-based robustness studies. The aim of this paper is to study the accuracy of the simplified scatter modeling methods in representing the real material variation. The simplified scatter modeling methods are evaluated by comparing the material scatter obtained by them to the scatter obtained by complete tensile tests, which are obtained after detailed timeconsuming experimental investigations. Furthermore, an accuracy assessment is carried out based on selected responses from an axially-crushed, square tube made from DP600 steel.

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Section: Methods 1amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An evaluation of simple techniques to model the variation in strain hardening behavior of steel

Shetty

Nilsson

2016

Struct Multidisc Optim

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“…Chen and Koç (2007) attributed the part-to-part variation to random variation of all uncontrollable process variables e.g. inherent equipment variation and blank dimension variation, within batch variation to the variation of controllable variables e.g.…”

Section: Variation Propagationmentioning

confidence: 99%

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

Govik¹

2014

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“…Advanced high strength steel (AHSS) has been widely used in the automotive industry for its light weight, crashworthiness, and productivity. However, it is difficult to achieve dimensional accuracy with AHSS because its higher elastic recovery and yield strength cause excessive springback [2]. Fabricating a target product shape with AHSS is challenging for part manufacturers, requiring a considerable amount of time as well as additional costs to modify tools for this springback.…”

Section: Introductionmentioning

confidence: 99%

Effect of Constitutive Equations on Springback Prediction Accuracy in the TRIP1180 Cold Stamping

Seo

Kim

Lee³

et al. 2017

Metals

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Abstract:This study aimed to evaluate the effect of constitutive equations on springback prediction accuracy in cold stamping with various deformation modes. This study investigated the ability of two yield functions to describe the yield behavior: Hill'48 and Yld2000-2d. Isotropic and kinematic hardening models based on the Yoshida-Uemori model were adopted to describe the hardening behavior. The chord modulus model was used to calculate the degradation of the elastic modulus that occurred during plastic loading. Various material tests (such as uniaxial tension, tension-compression, loading-unloading, and hydraulic bulging tests) were conducted to determine the material parameters of the models. The parameters thus obtained were implemented in a springback prediction finite element (FE) simulation, and the results were compared to experimental data. The springback prediction accuracy was evaluated using U-bending and T-shape drawing. The constitutive equations wielded significant influence over the springback prediction accuracy. This demonstrates the importance of selecting appropriate constitutive equations that accurately describe the material behaviors in FE simulations.

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Simulation of springback variation in forming of advanced high strength steels

Cited by 122 publications

References 26 publications

An evaluation of simple techniques to model the variation in strain hardening behavior of steel

An evaluation of simple techniques to model the variation in strain hardening behavior of steel

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

Effect of Constitutive Equations on Springback Prediction Accuracy in the TRIP1180 Cold Stamping

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