No abstract

Tube hydroforming (THF) is a frequently used manufacturing method in the industry, especially on automotive and aircraft industries. Compared with other manufacturing processes, THF provides parts with better quality and lower production costs. This paper proposes a design approach to estimate the T-shaped THF parameters, such as counter force, axial feed, and internal pressure, through finite element (FE) and artificial neural network (ANN) modeling. A numerical database is built through Taguchi's L27 orthogonal array of experiments to train the ANN. The micromechanical damage model of Gurson-Tvergaard-Needleman is used with an elastoplastic approach to describe the material behavior. This study aims to find the combinations of THF parameters that maximize the bulge ratio and minimize the thinning ratio and wrinkling. The numerical results obtained by the FE model show good correlation with the results predicted by the ANN.

No abstract

Cavitation is the main defect encountered during superplastic forming (SPF) of thin sheet aluminum alloys. In the present paper, the influence of preforming operation (cold or hot) on the superplastic forming ability and quality of 1.6-mm-thick sheet of 5083 SPF aluminum alloy is investigated. Specifically, grain size evolution and the characteristics of the cavitation process are discussed as a function of prior deformation and the preforming temperature. Optical and field emission gun scanning electron microscopy (FEG-SEM) were used to study the characteristics of the cavities and microstructure evolution. Image processing was used to measure the surface and volume fractions of the cavities. The results indicate that hot preforming leads to a lower number of cavities per unit surface compared to cold preforming prior to the SPF operation. However, the average cavity sizes and the average grain size are higher in the case of hot preforming compared to cold preforming, which lead to higher susceptibility to crack formation and reduced SPF ability of the alloy.

The hydroforming process is characterized in these recent years by the remarkable development to compare with other processes manufacturing such as deep drawing and bending... Hydroforming is a reliable process that improves the resistance and rigidity of parts with the geometrical and dimensional tolerances allowing for lower costs tool and therefore an overall cost of manufacturing reduced. The success of hydroforming process requires the control simultaneously of various parameters such as used material properties, thickness, internal pressure,... In this paper, we introduce our model based in a neural approach (ANN) compared to the numerical simulation and experimental results. This method allows a better thickness distribution during Tee extrusion tube hydroforming process (THF) and the optimization of the final part geometry. A multilayer's neural networks (MNN) program is used to control the tube wall thickness variation, so the loading paths (axial feeding and the internal pressure) are used like inputs for our networks, the thickness is the output.