Spatial variable curvature metallic tube bending springback numerical approximation prediction and compensation method considering cross-section distortion defect

Wang, Zili; Lin, Yaochen; Qiu, Lemiao; Zhang, Shuyou; Fang, Dingyu; He, Ci; Wang, Le

doi:10.1007/s00170-021-08051-w

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…Then, with the process parameter x and the profile parameter y, sample data can be obtained as D i = (x i , y i , ∆R i ), assuming that D 1 = (x 1 , y 1 , ∆R 1 ) and D 2 = (x 1 , y 1 , ∆R 1 ) are two different samples in the data set. Its similarity is defined as Equation (4).…”

Section: Construction Of Density Clustering Featuresmentioning

confidence: 99%

“…The results showed that the stress, strain, and springback of each pass based on the increment of the forming angle under the condition of five boundaries in the roll-forming process were smaller than those of other forming angle distribution methods. To obtain a higher forming accuracy of spatially variable-curvature bending metal tubes, Wang et al [4] proposed a numerical approximate springback prediction and compensation method considering SVC MT section distortion. The experimental results showed that the position deviation of each node was less than 1.4% and the average position deviation was less than 0.80% after springback compensation.…”

Section: Introductionmentioning

confidence: 99%

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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: Construction Of Density Clustering Featuresmentioning

confidence: 99%

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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“…Mai Rui proposed a compensation mechanism based on the principle of implicit equation iteration, so that the final shape of the part converges to the target shape through iterative finite compensation [24]. Wang Zili and others provided a numerical approximation springback prediction and compensation method for SVC MT, compared the calculation accuracy of the three springback numerical approximation methods, and concluded that the Runge-Kutta method has the highest prediction accuracy [25]. Wu Peijun and others used DynaForm software to perform springback simulation experiments on L-shaped parts and analyzed the thickness, edge pressing force, and corner radius of the concave and convex die by orthogonal test method [26].…”

Section: Introductionmentioning

confidence: 99%

Research on Structural Parameter Optimization and Springback Compensation Methods of a Steel Bar Hoop Bending Machine

Wei,

Ren,

Zhou

et al. 2023

Applied Sciences

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After being processed into a certain shape by a stirrup bending machine, steel bars are used as the skeleton of reinforced concrete in construction. The accuracy of their bending directly affects the bearing capacity of reinforced concrete. In order to improve the accuracy of the springback angle of steel bars after bending, this paper focuses on optimizing the structural parameters of the steel bar bending machine and controlling the springback angle of steel bars. The influence of the support shaft diameter, bending crankshaft diameter, and bending crankshaft radius on the springback angle of steel bars is analyzed using ANSYS explicit dynamic simulation and experimental analysis. By combining orthogonal experimental methods, the bending structural parameters were optimized, including a support shaft diameter of 20 mm, a crankshaft bending diameter of 40 mm, and a crankshaft bending radius of 80 mm. This can effectively reduce the springback angle of the steel bars and improve the accuracy of the bending angle after bending. In addition, in order to improve the control accuracy of the steel bar springback angle, a cubic spline interpolation method was proposed, and the collected steel bar springback angle data were fitted using Matlab. At the same time, comparisons were made between the least squares method, polynomial interpolation method, and cubic spline interpolation method. The results show that the compensation formula obtained by using the cubic spline interpolation method can effectively improve the control accuracy of the steel bar bending springback angle.

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“…Razali et al [23] proposed a springback prediction method for an accurate, fully implicit elasto-plastic finite element analysis function and multi-body processing scheme based on a tetrahedral micro-mesh system, which had an average error rate and maximum error rate of 8.1% and 12.4%, respectively. Wang et al [24] proposed a numerical approximate springback prediction and compensation method by considering the section distortion of a spatially variable curvature tube, establishing the curvature and torsion mapping functions of the central axis of the tube before and after springback. After springback compensation, the position deviation of each node was less than 1.4%, and the average position deviation was 0.80%.…”

Section: Introductionmentioning

confidence: 99%

Springback Prediction of Free Bending Based on Experimental Method

Hong

Zhang

2023

Applied Sciences

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The springback phenomenon has an impact on tube-processing quality during the free-bending process. For different pipe benders, the forming process is affected by different factors, such as gaps between the tube and the bending die and friction between the tube and the guiding mechanism and the bending die; thus, the theoretical U-R relationship cannot complete the precise processing required. In order to overcome the influence of springback during the free-bending forming process, a U-R experiment was performed first, and the actual U-R relationship was established. The U-R experiment demonstrated that when the bending die offset U increased, the tube-bending radius R decreased. Then, based on the results of the springback experiments, a springback prediction method was presented. The experimental method successfully predicted springback, demonstrating that the springback problem can be effectively solved through compensation of the arc section.

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Spatial variable curvature metallic tube bending springback numerical approximation prediction and compensation method considering cross-section distortion defect

Cited by 14 publications

References 29 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

Research on Structural Parameter Optimization and Springback Compensation Methods of a Steel Bar Hoop Bending Machine

Springback Prediction of Free Bending Based on Experimental Method

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