The multi-objective optimization of the loading paths for T-shape tube hydroforming using adaptive support vector regression

Abstract

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.

Dimensional optimization of variable thickness tube in T-shaped tube hydroforming using response surface methodology

“…The errors between the predicted and the numerical results for the BH and TR were approximately -4.3% and 9.1%, respectively. These errors are within reasonable tolerances (±10 %) indicating that the RSM models can be used to predict the objectives effectively [6].…”

“…Furthermore, the structural stiffness and stability of the parts can also be improved through THF [4]. Hence, several reports have explored the use of THF method in the production of T-and Y-shaped tubes [5][6][7]. In THF process, bursting is generally one of the main defects due to excessive thinning of the tubes.…”

“…In fact, high BH with low TR is desirable as it indicates good formability of the tube in THF process. The tube formability for THF is affected by geometrical factors and material properties [6,8]. Alaswad et al [9] studied the influence of geometrical factors on the thickness reduction and bulge height during the hydroforming of T-type bi-layered parts.…”

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

“…Response variables selected in their study were thinning and protrusion height and the best optimal combination of parameters were attained by the considered method. In another work, the authors considered the contact pressure of tube and counter punch as an additional parameter along with thinning ratio and protrusion height were considered to formulate the multi-objective problem [18]. Chebbah and Leebal [19] developed a new surrogate model to optimize the parameters like thinning variation and also the industrial requirements.…”

A new method to produce T-shaped tubular fittings with experimental and simulation results

“…To improve tube formability in THF processes, researchers applied several techniques to reduce thinning of the tube wall and to increase the possible height of the emerging branch. Huang et al [12] used the adaptive support vector regression during the T-shaped THF process to optimize the loading path. In a different research, Hwang et al [13] suggested a control algorithm to identify suitable loading paths in the T-shaped THF process with various outlet diameters.…”