Optimization of process parameters during hydroforming of tank bottom using NSGA-III algorithm

Zhang, Zaifang; Xu, Feng; Sun, Xiaomin

doi:10.1007/s00170-021-08057-4

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…There are many reasons for the cracking of sheet hydroforming, while FLD describes ultimate strain of sheet's cracking under different conditions. When the point's principal strain falls above the forming limit curve (FLC), 4,30,31 the part will break when it is formed. When the point's principal strain is below the FLC curve, the farther away the point is, the safer it is.…”

Section: Determination Of Design Variablesmentioning

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:

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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.

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Section: Determination Of Design Variablesmentioning

confidence: 99%