The optimization of the loading path for T-shape tube hydroforming using adaptive radial basis function

Huang, Tianlun; Song, Xiaojie; Liu, Min

doi:10.1007/s00170-015-7534-z

Cited by 19 publications

(7 citation statements)

References 40 publications

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“…The use of RSM to analysis THF process can be referred to [30,31]. The authors experienced the application of dual RBF for multi-objective robust optimization and the use of adaptive RBF to optimize the loading path for T-shape THF process [32,33]. An [34,35] used KRG to accelerate calculation speed in multi-objective optimization in THF process.…”

Section: Introductionmentioning

confidence: 99%

The multi-objective optimization of the loading paths for T-shape tube hydroforming using adaptive support vector regression

Huang

Song

Liu

2016

Int J Adv Manuf Technol

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The objective of this study is to introduce adaptive support vector regression, whose accuracy and efficiency are illustrated through a numerical example, to determine the Pareto optimal solution set for T-shape tube hydroforming process. A validated finite element model developed by the explicit finite element code LS-DYNA is used to conduct virtual T-shape tube hydroforming experiments. Multiobjective optimization problem considering contact area between the tube and counter punch, maximum thinning ratio, and protrusion height is formulated. Then, the Latin hypercube design is employed to construct the initial support vector regression model, and some extra sampling points are added to reconstruct the support vector regression model to obtain the Pareto optimal solution set during each iteration. Finally, the ideal point is used to obtain a compromise solution from the Pareto optimal solution set for the engineers.

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Section: Introductionmentioning

confidence: 99%

The multi-objective optimization of the loading paths for T-shape tube hydroforming using adaptive support vector regression

Huang

Song

Liu

2016

Int J Adv Manuf Technol

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“…The Gaussian function is selected as basis function because of its effectiveness and applicability in an approximate model. 22,23 Hence, the RBF can be defined as ϕi(‖x−bold-italicxi‖)=exp⁡true(−‖x−bold-italicxi‖2c2true), where c2 is the constant value of 10, bold-italicx is the design variable vector, bold-italicxi is the vector of the design variable at sample point i, ‖x−bold-italicxi‖ is the Euclidean distance, and m is the number of sample points.…”

Section: Analysis and Optimizationmentioning

confidence: 99%

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Zhi-yong

Zhang

Huang

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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Structure design for reducing noise is essential and widely studied because cab interior noise seriously affects the ride comfort of the driver and passengers, especially for heavy commercial vehicles. This paper proposes an enhancement investigation on the low-noise structure performance of a cab in a heavy commercial vehicle based on an approximation model. A series of vibroacoustic tests with varying running speeds are implemented first to acquire the vibration signals at four cab suspensions and the sound pressure signal at the right ear of the driver. The finite element model and structure-acoustic coupled model of the cab of a heavy commercial vehicle are established sequentially. When the vibration accelerations measured in the tests are converted into excitation signals using the cab structure-acoustic coupled model, the sound pressure of the right ear of the driver is predicted, and the accuracy of the cab structureacoustic coupled model is then verified. After panel acoustic contribution analysis, an approximation model of critical panel thickness and the peak noise near the right ear of the driver is established in accordance with the radial basis function. Genetic algorithm is utilized to solve an optimization model to obtain optimal panel thicknesses. By comparing the right ear sound pressure before and after optimization, the results confirm that optimized panel thicknesses can reduce sound pressure level of the critical frequency.

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“…20,21 We analyzed the trend of thickness distribution around the fillet under the influence of load paths. Fixation pressure P = 45 MPa, loading time t = 3 s, and friction coefficient m = 0.17.…”

Section: Effect Of Load Pathsmentioning

confidence: 99%

Analysis and experiment on tube hydroforming in a rectangular cross-sectional die

Jia

Luo

2017

Advances in Mechanical Engineering

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Rectangular section parts created through hydroforming have good application prospects in the automotive industry because of their advantages of simple manufacturing procedure, high forming quality, and greater bending module than circular cross-sectional parts. Forming quality is directly influenced by the formed corner parameters of rectangular section parts. It is also a difficult matter in rectangular section hydroforming. The hydroforming rules of an aluminum alloy circular tube are regarded as the research object in this study to investigate the rules of square cross-sectional parts in the hydroforming process. First, the mechanical parameters of the molding material are obtained through experiments. Second, a hydroforming experimental system is self-designed, and tube hydroforming without axial feed is investigated to determine the key parameters of tube forming. Simulation analysis is then conducted for the entire process of tube hydroforming using the nonlinear finite element software DYNAFORM. The finite element model and the correctness of the calculation method are verified by comparing their results with the experimental results. Thereafter, the influence of internal pressure, friction coefficient, and loading path on pipe forming quality is examined. Necessary references are provided for engineering applications.

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The optimization of the loading path for T-shape tube hydroforming using adaptive radial basis function

Cited by 19 publications

References 40 publications

The multi-objective optimization of the loading paths for T-shape tube hydroforming using adaptive support vector regression

The multi-objective optimization of the loading paths for T-shape tube hydroforming using adaptive support vector regression

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Analysis and experiment on tube hydroforming in a rectangular cross-sectional die

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