Tube bending and hydroforming of aluminium alloy S-rails

Oliveira, D. A.; Worswick, Michael J.

doi:10.1007/s12289-009-0404-1

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…(1) Though so many advanced constitutive models are proposed, the most widely used plasticity theories are still the classical ones so far, such as the Hill'1948 was used in reference [42][43][44][45], the Barlat'1989 was used in reference [48][49][50][51]71,74], the nonlinear hardening models of Armstrong-Frederick and Chaboche or their modified types were used in reference [70,83,86,[98][99][100]106,109]. Two reasons are considered can give explanations.…”

Section: Discussionmentioning

confidence: 99%

“…Most of the application objects are the drawbending sheet [10,23,33,34,[42][43][44]56,70,77,85,86,97,102,105,118] or stamping sheet [19,50,51], while researches about other forming methods and other profiles such as tube sections [30,48,60] are rare too. These phenomena will limit the promotion of the advanced constitutive models, and finally lead to the out of line for theory development and practical application.…”

Section: Discussionmentioning

confidence: 99%

“…It is used broadly for its yield surface is consistent with the yield surface calculated by crystallographic. By using the Barlat'1989 yield criterion, Oliveira and Worswick [48] simulated the springback of s-rails for circular 5XXX-series aluminum tube; Yu and Lin [49] studied the variation of springback curvature with forming parameters for the U-shaped aluminum profile of rotary-draw bending; Li [50] researched how to reduce the springback of torsion rail stamping of DP800 sheet. The Barlat'1989 yield criterion was also used in the work of Marretta and Lorenzo [51], to study the impact of material properties on springback of S-shaped AA6016 sheet in U-channel stamping process.…”

Section: The ''Hosford Family'' Criteriamentioning

confidence: 99%

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Development and application of the material constitutive model in springback prediction of cold-bending

et al. 2012

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: The ''Hosford Family'' Criteriamentioning

confidence: 99%

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Development and application of the material constitutive model in springback prediction of cold-bending

et al. 2012

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“…Oliveira, D. A. and Worswick, M. J. [8] studied the effects of tube bending and hydroforming processes on the appearance of the aluminum alloy S-rail structure by using a full-function tube bending machine and mandrel with a hydroforming press of 1,000 tons, to determine the effect of bending severity on the thickness and stress distribution within the tube. They used Finite Element Method (FEM) to simulate tube bending and hydroforming processes.…”

Section: Introductionmentioning

confidence: 99%

Springback optimization for CNC tube bending machine based on an artificial neural networks (ANNs)

Kongnoo,

Sonthipermpoon,

Wannarumon

2023

FME Transactions

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Predicting the springback angle has become the major production problem among tube benders. Springback is where the tube on a mandrel-less rotary draw bending tends to bounce back after being bent when the clamps are released. Accurately predicting the springback angle is crucial for effective tube bending. Machine learning (ML), a popular prediction approach, was applied to functions such as prediction or function approximation, pattern classification, clustering, and forecasting. To achieve this, the springback angle values from 27 experiments were collected and used as input into artificial neural networks (ANNs) in one area of ML. This research was conducted to study the optimization of the springback angle when bending ASTM A-210 Gr. A1 seamless tube with an outside diameter of 44.45 mm, using the 4 input factors Wall Thickness, Bending Radius, Dwell Time, and Bending Angle. The results showed that all factors significantly influence the springback angle in the tube bending process; different prediction methods were analyzed by comparing the results using different activation functions. The results showed that the optimal neural network architecture is 4-98-1; these results were achieved using the Sigmoid function, giving the lowest mean squared error (MSE) = 0.001892. The resulting coefficient of determination (R2 ) = 99.42%, the ReLU function R2 = 98.99%, the TanH function R2 = 98.53%, and the Identity function, which was 79.53%. It was also found that the best prediction of the springback angle using the best regression equation, with R2 = 82.32%, was better than the prediction using the 65 neurons with the Identity function R2 = 79.53%, a 2.79% difference in favor of the regression equation.

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“…Fracturing caused by over-thinning of the outside tube is a major failure in the manufacturing of bending tube, especially during the thick tube forming process [3] . Therefore, it is important to consider a low thinning ratio in the tube bending process.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization for Tube Bending Process Based on Finite Element Method

Chen

Liu

Zhang

2012

KEM

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The main causes of performance variation in tube bending process are variations in the mechanical properties of material, initial tube thickness, coefficient of friction and other forming process parameters. In order to control this performance variation and to optimize the tube bending process parameters, a robust design method is proposed in this paper for the tube bending process, based on the finite element method and the Taguchi method. During the robust design process, the finite element analysis is incorporated to simulate the tube bending process and calculate the objective function value, the orthogonal design method is selected to arrange the simulation experiments and calculate the S/N ratio. Finally, a case study for the tube bending process is implemented. With the objective to control tube crack (reduce the maximum thinning ratio) and its variation, the robust design mathematical model is established. The optimal design parameters are obtained and the maximum thinning ratio has been reduced and its variation has been controlled.

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Tube bending and hydroforming of aluminium alloy S-rails

Abstract: This research examines the effect of the tube bending and hydroforming processes on the characteristics of aluminium alloy s-rail structures used in crash applications.

Cited by 7 publications

References 19 publications

Development and application of the material constitutive model in springback prediction of cold-bending

Development and application of the material constitutive model in springback prediction of cold-bending

Springback optimization for CNC tube bending machine based on an artificial neural networks (ANNs)

Robust Optimization for Tube Bending Process Based on Finite Element Method

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