Application of simulated annealing method to pressure and force loading optimization in tube hydroforming process

Mirzaali, Mohammad J.; Seyedkashi, S. M. H.; Liaghat, Gholamhossein; Naeini, Hassan Moslemi; Moon, Young Hoon

doi:10.1016/j.ijmecsci.2011.12.005

Cited by 54 publications

(13 citation statements)

References 19 publications

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“…Intarakumthornchai et al [20] applied genetic algorithm (GA) to obtain and determine the loading path for fuel filler THF process. 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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“…Three metaheuristic optimization algorithms (i.e., GA, SA, and WCA) were employed to optimize the transit models for the benchmark data set. These optimizers have illustrated their capability as efficient optimization tools with great potential for solving optimization problems [25,28,29]. The transit model and the reported optimizers were coded and run in MATLAB programming software provided by Mathworks (Natick, MA, USA).…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Optimal Coordination Strategy for an Integrated Multimodal Transit Feeder Network Design Considering Multiple Objectives

Almasi

Sadollah

et al. 2018

Sustainability

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Public transportation can have an efficient role ingainingtraveler satisfaction while decreasing operation costs through establishing an integrated public transit system. The main objective of this research is to propose an integrated multimodal transit model to design the best combination of both railway and feeder bus mode transit systems, while minimizing total cost. In this paper, we have proposed a strategy for designing transit networks that provide multimodal services at each stop, and for consecutively assigning optimum demand to the different feeder modes. Optimum transit networks have been achieved using single and multi-objective approaches via metaheuristic optimization algorithms, such as simulated annealing, genetic algorithms, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The used input data and study area were based on the real transit network of Petaling Jaya, located in Kuala Lumpur, Malaysia. Numerical results of the presented model, containing the statistical results, the optimum demand ratio, optimal solution, convergence rate, and comparisons among best solutions have been discussed in detail.

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“…Abedrabbo et al [12] employed genetic algorithm by an optimization software in combination with finite element method to optimize the loading paths in tube hydroforming process. 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.…”

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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Application of simulated annealing method to pressure and force loading optimization in tube hydroforming process

Cited by 54 publications

References 19 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

Optimal Coordination Strategy for an Integrated Multimodal Transit Feeder Network Design Considering Multiple Objectives

Application of a new integrated optimization approach in sheet hydroforming process

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