Integration of gradient based and response surface methods to develop a cascade optimisation strategy for Y-shaped tube hydroforming process design

Abstract

In the last years a strong research effort was produced in order to develop and design new forming technologies able to overcome the typical drawbacks of traditional forming operations. Among such new technologies, hydroforming proved to be one of the most promising. The design of tube hydroforming operations is mainly aimed to prevent bursting or buckling occurrence and such issues can be pursued only if a proper control of both material feeding history and internal pressure path during the process is performed. In this paper, a proper optimisation strategy was developed on Y-shaped tube hydroforming process which is characterized by a quite complex process mechanics with respect to axi-symmetric tube hydroforming operations. The design procedure was aimed to properly calibrate the internal pressure histories. The basic idea, in this paper, is to integrate a steepest descent method with a moving least squares approach in order to reach the optimal internal pressure curve in the hydroforming of an Y-shaped steel tube. Thus, a cascade optimisation procedure was implemented which consisted of two optimisation steps: the former is focused on the application of a steepest descent method, the latter is based on a response surface approach utilising a moving least squares approximation. The cascade procedure was driven by the will to reduce the total number of numerical simulations necessary to reach the optimum with respect to other optimisation methods

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

“…Brooghani et al [26] optimized the loading path for T-shape THF process using multi-level RSM. RSM-based multi-objective optimization of the loading path for THF process is studied in [27][28][29]. The use of RSM to analysis THF process can be referred to [30,31].…”

A fast algorithm for strain prediction in tube hydroforming based on one-step inverse approach

“…If one of these parameters is not carefully adjusted, defects such as bursting or wrinkling may appear according to Langa et al (2009), Di Lorenzo et al (2010). Since the recent past, the feasibility of a product was mainly carried out by engineers using trial-and-error iterative procedures that are expensive and error prone.…”

A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

“…Abedrabbo et al [5] used genetic algorithm search methods to identify the best loading paths in combination with the nonlinear structural finite element code LS-DYNA. Di Lorenzo et al [6] developed a cascade optimisation procedure based on a steepest descent method and the RSM utilising a moving least squares approximation to find the optimal internal pressure curve. Ahmadi Brooghani et al [7] proposed an efficient technique to optimise loading path based on the multilevel RSM in which the optimisation process can be continued as a multilevel process.…”