Research on roll forming process and springback based on five-boundary condition forming angle distribution function

Su, Chunjian; Liu, Jiazhen; Zhao, Zexuan; Lou, Shumei; Wang, Rui; Yang, Longyun

doi:10.1007/s12206-020-1121-4

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Based on the experimental results, an analytic equation was established to explain the effect of grain size on magnesium springback. Su et al [3] proposed the five-boundary conditional distribution function of the forming angle. The stress, strain, and springback of the sheet during roll forming under three different angle distribution functions were studied.…”

Section: Introductionmentioning

confidence: 99%

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

Cao

Liu

et al. 2023

Metals

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Roll-bending technology has a high degree of flexibility and does not require special molds. However, based on the existing plastic mechanics theory and finite element simulation, it is difficult to accurately analyze the complex spatial relationship of profile roll forming. Therefore, a fixed-curvature prediction model is constructed based on XGBoost (extreme gradient boosting), and the coupling effect of the process parameters and material performance parameters on the roll-forming process is explored. Combined with a Bayesian optimization algorithm, the hyperparameters of the fixed-curvature prediction model are optimized. In addition, based on the prediction result of the fixed curvature, a variable-curvature prediction model is established using the conditional random field (CRF). To further improve the prediction accuracy, an error compensation network is added after the result of the CRF in order to map the discrete sequence to the continuous sequence. The experimental results show that the mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) predicted by the models above are much smaller than other methods, which verifies the superiority of the prediction models.

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Section: Introductionmentioning

confidence: 99%

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

Cao

Liu

et al. 2023

Metals

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“…Cheng et al [17] developed the UDT (USTB-Durable T) -type angle adjusting method to compensate springback with the improvement of the edge waves and asymmetric behavior. Su et al [18] proposed a five-boundary conditional distribution function of the forming angle to decrease stress, strain, and springback at each forming stand. Abeyrathna et al [19] presented a first step towards an in-line shape compensation method to estimate shape defects and the subsequent re-adjustment of tooling for compensation.…”

Section: Introductionmentioning

confidence: 99%

Optimization design of process parameters for cold and hot composite roll forming of the AHSS square tube using response surface methodology

Wang

Mehari²,

Wu³

et al. 2022

Int J Adv Manuf Technol

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Advanced high-strength steel (AHSS) is a highly competitive material for the automobile industry to resolve the challenge of weight reduction and passenger safety. With the increase in strength, defects such as microcrack, local thickness reduction, and large corner radius in the corner section of cold roll-formed steel products become more and more serious. Cold and hot composite roll forming (CHCRF) is a newly developed manufacturing technology that combines the advantages of cold and hot forming to overcome the challenge of forming AHSS. In this paper, the mathematical models that represent the effect of line velocity, heating power, and deformation amount on the yield strength, outer corner radius, and microcrack length of the corner section of cold and hot composite roll-formed AHSS square tube are investigated by the response surface methodology (RSM) with Box-Behnken design (BBD). From the analysis of variance (ANOVA), it is found that all the three process parameters play a significant role in the outer corner radius and microcrack length, while the line velocity and heating power influence the yield strength significantly. Furthermore, an optimization process is proposed, with the outer corner radius and microcrack length as objective functions and the yield strength as a constrained condition, to manufacture the AHSS square tube with a small outer corner radius and no microcrack defect.Experimental verification has been conducted using the optimal process parameters, and the average magnitude of error is about 5 %, which implies that the optimization design is accurate and reliable.

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“…Wang [33] showed that the necessary condition for longitudinal bending deformation is the linear distribution of longitudinal fibers. Su [34,35] studied the roll forming process and springback based on a five-boundary condition-forming angle distribution function and analyzed the effects of plate thickness, forming angle and material yield strength on stress, strain and springback angle.…”

Section: Introductionmentioning

confidence: 99%

A Method for Rapid Prediction of Edge Defects in Cold Roll Forming Process

Meng

Fang

2021

Mathematics

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In order to implement rapid prediction of edge defects in the cold roll forming process, a new analytical method based on the mean longitudinal strain of the racks is presented. A cubic spline curve with the parameters of the cumulative chord length is applied to fit the corresponding points and center points of different passes, and fitting curves are obtained. As the cold roll forming is micro-tension forming, the tensions between racks are ignored. Then the mean longitudinal strains between racks are obtained. By comparing the mean longitudinal strain between racks and the yield strain of the material, we can judge whether there are defects at the edges. Finally, the reasonableness of this method is illustrated and validated by an example. With this method, the roll forming effect can be quickly predicted, and the position where a greater longitudinal strain occurred can be determined. In order to prevent the defects, the deformation angles need to be modified when the result is beyond the yield strain. To further prove the correctness of the theory, the results of the analytical method are compared with the ones of the non-linear finite element software ABAQUS. The analytical results have the same trend as the finite element results. This method can provide useful guidance to the actual design process.

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Research on roll forming process and springback based on five-boundary condition forming angle distribution function

Cited by 7 publications

References 15 publications

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

Optimization design of process parameters for cold and hot composite roll forming of the AHSS square tube using response surface methodology

A Method for Rapid Prediction of Edge Defects in Cold Roll Forming Process

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