A Kriging-based non-probability interval optimization of loading path in T-shape tube hydroforming

Huang, Tianlun; Song, Xiaojie; Liu, Min

doi:10.1007/s00170-015-8034-x

Cited by 13 publications

(8 citation statements)

References 66 publications

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“…3 Application to the T-shape THF process 3.1 FE model validated the experimental results [49,50] A validated FE model developed by the explicit FE code LS-DYNA [51] is employed to simulate the T-shape THF process, as shown in Fig. 7.…”

Section: Numerical Examplementioning

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: Numerical Examplementioning

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

Self Cite

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“…Multi objective optimization can be performed in probabilistic or non-probabilistic environments and can take tolerances and variation into account. Having a probability function for the variables in the hydroforming process produces better results in finite element analysis [48] but is not required and other recent examples of using only the bounds of uncertainty have also been successful [49]. Success has been had with simulations that use fuzzy logic, simulated annealing, and traditional modelling techniques and so selection choose comes down to a mix of application, complexity, and experience.…”

Section: Analytical Methods and Numerical Simulationsmentioning

confidence: 99%

“…Several studies cite process variability as a key difficulty in load path optimization and have proposed various means to take this into account. For example, Abdessalem et al use a probabilistic approach to account for variation during load path creation [48], while Huang et al propose a kriging-based non-probability system [49] which only requires the bounds of uncertainty instead of a probabilistic function (presumably because this information is generally easier to acquire). Other studies tried to optimize load paths with fuzzy logic, [50] or by statistical means [51] or with metamodeling techniques to cut down on computational time [52].…”

Section: Process Windows and Loading Pathsmentioning

confidence: 99%

A state of the art review of hydroforming technology

et al. 2019

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Hydroforming is a relatively new metal forming process with many advantages over traditional cold forming processes including the ability to create more complicated components with fewer operations. For certain geometries, hydroforming technology permits the creation of parts that are lighter weight, have stiffer properties, are cheaper to produce and can be manufactured from fewer blanks which produces less material waste. This paper provides a detailed survey of the hydroforming literature of both established and emerging processes in a single taxonomy. Recently reported innovations in hydroforming processes (which are incorporated in the taxonomy) are also detailed and classified in terms of “technology readiness level”. The paper concludes with a discussion on the future of hydroforming including the current state of the art techniques, the research directions, and the process advantages to make predictions about emerging hydroforming technologies.

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“…Experimental results showed that proposed adaptive method can be used for process optimization of large search space. Huang et al [26] used the Kriging method to build proxy model for loading path design of T-tube hydroforming, and the robustness and reliability of this method were verified. Intarakumthornchai et al [27] integrated genetic algorithms into finite element analysis for determination of feasible loading paths.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Process Parameters during Hydroforming of Tank Bottom using NSGA-III Algorithm

Zhang

Sun

2021

Preprint

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The hydroforming technology can realize overall forming of large storage tank’s bottom, but the quality is affected by many technological parameters. In view of wrinkling and cracking defects of integral storage tank’s bottom in hydroforming, a multi-objective optimization model is established for process parameters include pre-expansion pressure, hydraulic pressure, blank holder force and fillet radius of blank holder. Based on finite element simulation, the surrogate model between process parameters and quality criteria is established using Kriging technique. NSGA-III is used to determine optimal process parameters when storage tank’s bottom reaches targets include minimum wall thickness variations, minimum fracture trend, minimum flange wrinkle and minimum wrinkle trend. Compared with Particle swarm optimization (PSO) algorithm, NSGA-III algorithm is more suitable to solve this optimization problem. The validity of this method and accuracy of the results are verified by simulation experiments.

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A Kriging-based non-probability interval optimization of loading path in T-shape tube hydroforming

Cited by 13 publications

References 66 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

A state of the art review of hydroforming technology

Optimization of Process Parameters during Hydroforming of Tank Bottom using NSGA-III Algorithm

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