| description abstract | Springback is one of the factors that causes decreased product quality in tube bending; 2D and 3D stretch bending can be used to manufacture a complex geometry of a tube with springback reduction. For a nonlinear springback problem in tube bending, an artificial neural network (ANN) is an attractive data-driven approach to achieving springback prediction and control. The main objective of this paper is to control springback and improve geometrical quality with an ANN in 2D and 3D stretch bending of a rectangular tube. In general, an ANN is trained with collected data sets from a large number of experiments, causing expensive costs and time-consuming work. In the present work, the training data sets for the proposed ANN are obtained from both experiments and an analytical springback model. As the analytical model can adopt different bending angles, material properties, and geometries, supplementary data by the analytical model significantly reduced the number of experiments needed for ANN training. Contrary to the typical springback predictions, the proposed ANN synthesizes the machine settings based on the desired dimensions as the inputs. It is shown that springback can be controlled by specifying the bend angles provided by the ANN prediction. The proposed ANN method was validated in 2D and 3D stretch bending of a rectangular tube, and its prediction and control performance are favorably compared to an ANN trained with only experimental data sets. | |
