Loading path optimization of T tube in hydroforming process using response surface method

Feng, Yingying; Hong-ge, Zhang; Zhang, Luo; Wu, Qing

doi:10.1007/s00170-018-3087-2

Cited by 15 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results showed that this method could be used for process optimization in a large search space. Ying et al 10 used DYNAFORM software to simulate the forming performance of T-shaped pipes under different loading paths and obtained the simulation values of the forming performance parameters. According to the optimization evaluation criterion, the disturbance diagram, and the interaction effect of different experimental factors, the main factors are optimized, and the optimal value of the loading path is selected.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Zhang

Zhou

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

As a critical component of the propellant tank, the tank bottom is subjected to complex loads such as internal pressure and vibration and has high requirements for structural load-bearing capacity. Hydroforming deep drawing is one of the techniques for the integral forming of the tank bottom. As the tank bottom is a large-size thin-walled structure, defects such as cracks and wrinkles are prone to occur during the hydroforming deep drawing process. Aiming at reducing these defects, the hydraulic pressure loading path and blank holder force loading path of the hydroforming deep drawing process are studied, and a multi-objective optimization method is proposed to improve the surface accuracy and thickness distribution uniformity of the tank bottom. The complex loading path curve optimization problem is transformed into a functional relationship between hydraulic pressure and blank holder force with time. The hydraulic pressure and blank holder force at each time node are used as design variables, and the maximum wall thickness reduction rate, rupture trend factor, wrinkle height, and wrinkle trend factor are used as optimization targets. The radial basis function (RBF) neural network is used to establish the approximate model between the loading path and the optimization target, and the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the solution. Taking the hemispherical tank bottom as an example, the optimal hydraulic pressure loading path and blank holder force loading path are obtained, and the quality of the formed part is improved.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Zhang

Zhou

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…The proposed approach established better tool dimensions and loading paths to attain sound final product. Ying-Ying et al [20] worked on the optimization of loading paths by considering the geometrical parameters of T -Shaped tube hydroforming component. Other than loading paths, the authors considered backward displacement and frictional coefficient as also the effectible parameters to model the process.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Tubular Hydroforming Process Parametric Optimization using TOPSIS and PSI techniques

Pv¹,

Reddy

2021

Preprint

View full text Add to dashboard Cite

Tube Hydroforming (THF) process is widely used in the aerospace and automotive industries. The success of forming tubular components using the THF process generally depends on many influencing parameters such as geometry, coefficient of friction, loading pathways, material formability, etc. The objective of this study was to determine the effect of the corner radius, coefficient of friction and heat treatment temperature on the bulging of the tubular component using finite element simulations. To address the complexity of parameter selection correctly with different alternatives, the multi-criteria decision-making methods (MCDM) applied in the present work. Two decision models, namely Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and Performance Selection Index (PSI) methods with entropy weighting criteria were applied for obtaining the best combination of parameters to get a sound quality product. Finally, the proposed ranking was validated by conducting experimental trials and the error was observed to be in limits in comparison with the simulated data. ANOVA was also performed to distinguish the significant parameters and their contribution to the responses. Both the MCDM methods suggested the same favorable and unfavorable alternatives.

show abstract

Investigation of forming optimization of composite tubes based on liquid impact forming

Yao

Liu

Liang

et al. 2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Loading path optimization of T tube in hydroforming process using response surface method

Cited by 15 publications

References 25 publications

Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Multi-objective Tubular Hydroforming Process Parametric Optimization using TOPSIS and PSI techniques

Investigation of forming optimization of composite tubes based on liquid impact forming

Contact Info

Product

Resources

About