Optimization of loading path in hydroforming T-shape using fuzzy control algorithm

Teng, Bugang; Li, Kai; Yuan, Shijian

doi:10.1007/s00170-013-5086-7

Cited by 38 publications

(19 citation statements)

References 16 publications

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“…References [3][4][5][6][7][8][9] applied the adaptive simulation method, which is based on the ability to detect the onset and growth of defects during the process and promptly react to them, to obtain a set of feasible loading paths with a minimum number of simulation run or even with a single run. Strano et al [3] pointed out that when finite element (FE) simulation is used to reduce engineering effort and lead times, a reliable input data, namely the constitutive parameters of tubular material and lubrication conditions, for THF process simulation is needed.…”

Section: Introductionmentioning

confidence: 99%

“…The lubrication mechanisms that occur at the tool-workpiece interface for the transition and expansion zones are discussed [14,15]. Teng et al [5] optimized the loading paths for T-shape THF process based on a fuzzy control algorithm and FEM, and the result was validated against the experimental work. Manabe et al [6] used an intelligent technique to determine the optimal loading paths for the T-shape THF process with a counter punch.…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…Mirzaali et al [13,14] incorporated the simulated annealing (SA) algorithm and finite element method to obtain the optimal loading paths for hydroforming of copper tubes. Teng et al [15] developed an adaptive simulation approach integrated with a fuzzy control algorithm to optimize the loading path in T-shaped tube hydroforming. Seyedkashi et al [16,17] proposed an adaptive SA algorithm in conjunction with finite element simulations to determine the optimal pressure and feed loading paths in warm tube hydroforming.…”

Section: Introductionmentioning

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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“…Teng et al. 15 developed a simulation system integrated with fuzzy control algorithm to optimize the loading path of hydroforming a T-shaped tube.…”

Section: Introductionmentioning

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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Optimization of loading path in hydroforming T-shape using fuzzy control algorithm

Cited by 38 publications

References 16 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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