A Comparison between Three Meta-Modeling Optimization Approaches to Design a Tube Hydroforming Process

Abstract

Computer aided procedures to design and optimize forming processes have become crucial research topics as the industrial interest in cost and time reduction has been increasing. A standalone numerical simulation approach could make the design too time consuming while meta-modeling techniques enables faster approximation of the investigated phenomena, reducing the simulation time. Many researchers are, nowadays, facing such research challenge by using various approaches. Response surface method (RSM) is probably the most known one, since its effectiveness was demonstrated in the past years. The effectiveness of RSM depends both on the definition of the Design of Experiments (DoE) and the accuracy of the function approximation. The number of numerical simulations can be strongly reduced if a proper optimization approach is implemented: one of the main issues about optimization techniques is related to the design necessity of performing either global or local approximation. This paper aims to test the efficacy of some meta-modeling techniques in the optimization of a T-shaped hydroforming process. In this paper three optimization approaches based on different meta-modeling techniques are implemented. In particular, classical Polynomial Regression approach (PR), Moving Least Squares approximation (MLS) and Kriging method are applied. The results showed that, thanks to the peculiarities of MLS and Kriging methods, it is possible to strongly reduce the computational effort in sheet metal forming optimization, particularly in comparison with a classical PR approach. Differences were highlighted and quantified.