An analytical model for rapid prediction and compensation of springback for chain-die forming of an AHSS U-channel

Li, Yaguang; Liang, Zhonglin; Zhang, Zhiheng; Zou, Ting; Li, Dayong; Ding, Shou-Nian; Xiao, Hua; Shi, Lei

doi:10.1016/j.ijmecsci.2019.05.046

Cited by 35 publications

(9 citation statements)

References 42 publications

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“…Knowledge of the amount of elastic springback is necessary at the tool design stage. The idea of tool correction is to use compensation for elastic deformations by selected process parameters, the shape of the preform die or both dimensional and shape correction of the tool [ 11 , 12 ]. When designing the bending process, the critical bending radius should be taken into account.…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Trzepieciński

Lemu

2020

Materials

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This paper presents the results of predictions of springback of cold-rolled anisotropic steel sheets using an approach based on a multilayer perceptron-based artificial neural network (ANN) coupled with a genetic algorithm (GA). A GA was used to optimise the number of input parameters of the multilayer perceptron that was trained using different algorithms. In the investigations, the mechanical parameters of sheet material determined in uniaxial tensile tests were used as input parameters to train the ANN. The springback coefficient, determined experimentally in the V-die air bending test, was used as an output variable. It was found that specimens cut along the rolling direction exhibit higher values of springback coefficient than specimens cut transverse to the rolling direction. An increase in the bending angle leads to an increase in the springback coefficient. A GA-based analysis has shown that Young’s modulus and ultimate tensile stress are variables having no significant effect on the coefficient of springback. Multilayer perceptrons trained by back propagation, conjugate gradients and Lavenberg–Marquardt algorithms definitely favour punch bend depth under load as the most important variables affecting the springback coefficient.

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Section: Introductionmentioning

confidence: 99%

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Trzepieciński

Lemu

2020

Materials

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“…The coefficients from the previous study [22] are adopted and are listed in Table 2. A saturated elastic modulus of 180.5 GPa calculated from the LUL test [8] is used in this study.…”

Section: Methodsmentioning

confidence: 99%

“…In contrast with roll forming, chain-die forming increases the forming length by enlarging the radii of the rolls to tens of meters, which is realized by discrete die blocks attached to a rotational track board, as shown in Fig. 1 [8,9]. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental and numerical results show that a smaller longitudinal strain is achieved by chain-die forming, due to a smoother transitional surface, and it was also observed that chain-die forming can alleviate the longitudinal bow defects. Li et al [8] proposed an analytical model for rapid prediction and compensation of the U profile chain-die forming process.…”

Section: Introductionmentioning

confidence: 99%

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Springback and Longitudinal Bow in Chain-Die Forming U and Hat Channels

Liang

Liu

Zou

et al. 2021

Preprint

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Chain-die forming is an emerging sheet metal technique for manufacturing advanced high strength steel (AHSS) products. Springback and longitudinal bow are two of the major shape defects in gradual forming. In this study, the springback and longitudinal bow of AHSS in chain-die forming of hat and U profiles are investigated through experiment and finite element simulation. The disparity of springback along the longitudinal direction and disparate longitudinal bow signifies complex deformation processes of chain-die forming. Finite element simulation of the chain-die forming process gives an insight into the formation of non-uniform springback and longitudinal bow. The gradual forming process by one die block after another causes bending and reverse-bending deformation in the web area and the redundant deformation of the sheet metal, which further leads to non-uniform bending moment and accumulated stresses along the longitudinal direction. At the same time, the redundant deformation will also result in the longitudinal strain on the edge, while the downhill deformation decreases the maximum longitudinal strain at the edge and introduces the longitudinal strain on the web. The different non-uniform longitudinal strain distribution along the transversal direction causes disparate longitudinal bow behaviors for chain-die formed AHSS hat and U profiles, downward bowing and upward bowing respectively. By the established model, the disparate springback and longitudinal bow behavior can be determined, which also contribute to the process design for chain-die formed AHSS products.

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“…The first is an analytical model and the other one rests in the application of a finite element analysis (FEA). Analytical (or semi-analytical) modelling of springback can be found, e.g., in [19][20][21][22][23][24][25][26][27][28]. The greatest disadvantage of such an approach rests in the utilisation of some simplifications, so their accuracy is generally not so high compared to the results of FEA.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Computational Model and Mesh Strategy on the Springback Prediction of the Sandwich Material

2022

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The effect of the computational model and mesh strategy on the springback prediction of the thin sandwich material made of micro-alloyed steel was investigated in this paper. To verify the chosen computational strategy, a comparison of the experimentally obtained specimen (U-bending) with the FEA result was performed. The Vegter yield criterion combined both with the isotropic and kinematic hardening law was used for the calculation. In addition, the effect of the deformation mesh element (surface and volume) on the accuracy of the springback prediction was investigated. It was concluded that the choice of the volume deformation mesh does not significantly improve the accuracy of the results. Moreover, it is quite a time-consuming approach. The much greater influence was monitored by concerning the selection of hardening law, where the anisotropic one was more suitable to be used on the springback prediction of a given sandwich material.

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An analytical model for rapid prediction and compensation of springback for chain-die forming of an AHSS U-channel

Cited by 35 publications

References 42 publications

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm

Springback and Longitudinal Bow in Chain-Die Forming U and Hat Channels

Effect of the Computational Model and Mesh Strategy on the Springback Prediction of the Sandwich Material

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