The effect of tube dimensions on optimized pressure and force loading paths in tube hydroforming process

Seyedkashi, S. M. H.; Naeini, Hassan Moslemi; Liaghat, Gholamhossein; Mashadi, Mahmoud Mosavi; Mirzaali, Mohammad J.; Shojaee, K.; Moon, Young Hoon

doi:10.1007/s12206-012-0430-7

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…Mirzaali et al [21,22] used simulated annealing algorithm to find the optimal loading parameters in THF process with and without axial force loading conditions. Seyedkashi et al [23] studied the effect of tube dimension on optimized pressure and force loading paths in THF process using simulated annealing algorithm.…”

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 idea of SA was initiated by Metropolis et al [24] and used by Kirkpatrick et al [25] for the first time to solve an optimization problem. SA method is inspired from a heat treatment process called "annealing" in which a metal is heated up and then cooled down to a homogenous crystalline structure to reach the thermodynamic equilibrium [26]. SA uses a stochastic search technique to define the minimum energy for a goal function at each temperature.…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

Application of a new integrated optimization approach in sheet hydroforming process

Hashemi

Gollo

Seyedkashi

2018

Mechanics & Industry

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The purpose of this study is to produce a desired hydroformed product under the optimal pressure profile. To achieve this purpose, a new adaptive optimization approach is proposed based on fuzzy logic control (FLC) integrated with simulated annealing (SA) optimization technique and ANSYS parametric design language (APDL). An intermediate MATLAB code was developed and used to manage data transfer automatically between FLC, SA and APDL, in which there would be no need for any interaction of user/designer during the optimization process execution. This method aims to find the optimal pressure loading profile, prevent wrinkling and necking failures, reduce unsuccessful iterations, and enhance convergence precision. This method is capable of adaptively changing the process parameters in order to reach the optimized values with higher accuracy in a more reasonable time. The results show a good agreement between the proposed optimization approach and experiments. The developed optimization approach is a practical and reliable design tool for industrial production of any symmetric shell cups using hydroforming process.

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“…SA is inspired from a heat treatment procedure known as annealing, which leads a metal to a crystalline structure with larger size and lowest energy during the cooling process. 20 Therefore, the system's structure at the end of the process becomes more organized and closer to the steady-state condition. 21 The main advantage of SA is its ability to escape from local minima based on the acceptance rule of the system's energy.…”

Section: Parametric Design Techniquementioning

confidence: 99%

Adaptive hybrid optimization of hydrodynamic deep drawing with radial pressure process by combination of parametric design and simulated annealing techniques

Hashemi

Gollo

Seyedkashi

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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An adaptive hybrid simulated annealing technique with ANSYS parametric design language is developed to optimize hydrodynamic deep drawing assisted by radial pressure process. This work aims to determine an optimal pressure path by redefinition of simulated annealing parameters and creating an adaptive finite element code using ANSYS parametric design language for any cylindrical, conical, and conical-cylindrical cups. The simulated annealing algorithm is developed adaptively with respect to hydrodynamic deep drawing with radial pressure process to link with ANSYS parametric design language code using a script in MATLAB. Parametric definition of process parameters enables the optimization algorithm to change the finite element model configuration in each iteration. Defective product is detected by definition of two failure criteria based on thinning and wrinkling occurrence during the optimization process. The proposed optimization method is employed in fractional factorial design of experiment to investigate the effective parameters on final product quality. Also, a regression model is derived to predict the final product quality based on the maximum thinning percentage under the optimal pressure path. Reliability of the optimization procedure and regression model is validated by experiments.

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The effect of tube dimensions on optimized pressure and force loading paths in tube hydroforming process

Cited by 18 publications

References 23 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

Application of a new integrated optimization approach in sheet hydroforming process

Adaptive hybrid optimization of hydrodynamic deep drawing with radial pressure process by combination of parametric design and simulated annealing techniques

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