Papers by Author: Yong Jun Wang

Abstract: The shallow basin-shaped aluminum sheet part had buckling defects after the bladder forming. Analysis the buckling defects’ mechanism, according to these, we proposed one step bladder forming process, used rolling leveler which could reduced the buckling height to level sheet before bladder forming, and made experiments to compare the structure effect of buckling height among the four kinds of die structure. The results showed that the one step bladder forming process is valid, and used the original die with sheet edge fold when it was bladder forming.

Abstract: The heat transfer coefficient during the aluminum thin plate quenching is difficult to measure experimentally. In this paper, according to the warping deformation characteristics of the 2024 honeycombed aluminum thin plate quenching, the heat transfer coefficient is obtained using finite element software ABAQUS. During the calculation process of the heat transfer coefficient, the sheet practical quenching process of immersion and the air-cooling has been considered. Using the heat transfer coefficient above, the quenching temperature field is solved through the simulation. Based on the temperature field, the residual stress field is simulated. Depending on the simulation results, the magnitude and the distribution of the residual stress is obtained. By X - ray diffraction method, the simulation results have been compared to the experiment results and they are in better agreement. It proves that the simulation method is available and effectively.

Abstract: In this paper, algorithms are presented for predicting peen forming parameters of integral aircraft wing panels with complex airfoil shapes. The peen forming deformation is divided into stretching deformation and bending deformation. The stretching deformation is assumed to result from the tensile strain within the plane panel, and the bending deformation corresponds to the difference of maximum and minimum curvature of the airfoil surface. The distribution of the forming tensile strain within the panel is obtained by optimal mapping of the airfoil surface in the sense that the stretching deformation energy from the plane panel to its spatial shape is minimized. In order to fit the nonlinear relation between the peening parameters and the deforming parameters, a back-propagation (BP) artificial neural network (ANN) is modeled with input parameters of thickness, curvature, tensile strain, etc, to predict the peening parameters of coverage, air pressure and intensity. Experimental peen forming data are given to train the BP ANN. It’s verified that the predicting methods are effective.

Abstract: In extrusion stretch bending process, there are many factors which affect springback of the workpiece such as mechanical properties of the material, friction condition and process parameters. The springback of same batch of extrusion is different at same forming parameters because of the variation of the mechanical properties of the material and the friction condition. A method of intelligent control of springback in stretch bending process is proposed by using ANN(artificial neural networks). The online identification model of the mechanical properties of the material and friction coefficient and the online prediction control model of springback of workpiece in stretch bending process are established by using ANN ,which are trained by the data of analysis calculation. It realizes the intelligent control on springback of stretch bending to online identify the material properties and friction coefficient and predict springback and adjust process parameters dynamically through the whole process of stretch bending. The results from the experiment state that the intelligent control method can suit the variation of mechanical properties of material and friction condition and improve the geometry precision.