Papers by Author: Shi Hong Lu

Abstract: Ultrasonic peening forming (UPF) is a newly developed sheet metal forming technology which has been applied in the aerospace engineering. It has the advantages of low equipment cost, portability, no pollution and excellent comprehensive performance of the material after being formed. It will have a broad application in local forming and sizing of airplane wing panel. When the panel is relatively thinner, there will be two different types of instability in the panel surface after being formed, one is a local instability, and the other is a global instability. As to the local instability, an equal ratio partial structure of the wing panel was chose as the experiment research subject, which has the same thickness and local area dimension. The simulation of the ABAQUS software showed the variation of arc height for different thickness panel under different impact amplitude, the results showed that arc height changed negatively when the impact amplitude reached a specified value, namely global instability. When the thickness is up to 3.2mm, the arc height of panel increased linearly implied the global instability stopped. As to the local instability (spherical bulge phenomenon), a partial component with the same proportion substituted for typical complex flat panel. Control variable method is used to study the spherical bulge deformation in local panel region, the relationship between shot peening time and impact amplitude was established. The result showed that he larger impact amplitude, the smaller the impact time was when the local instability occurs. The research results have great guiding significance over the practical manufacturing process.

Abstract: One-axle rotary shaping with the elastic medium (RSEM) is a kind of advanced sheet metal forming process. The research object is the springback of aluminous U-section. The orthogonal method is used to arrange the simulation experiments, the forming and springback of the workpiece are simulated successfully with the Finite Element Simulation software, and The main factors influenced the RSEM are analyzed. The simulation results are used as the training samples of the artificial neural network (ANN), and the ANN prediction model of RSEM process is set up. The prediction results would be tested with the experiment data, and only a little tolerance was existed between the two values. It demonstrated that the combination of orthogonal test, numerical simulation and neural network could effectively predict the springback of RSEM, the design efficiency of process parameters would be improved. It would guide the development of precision forming technology.

Abstract: Bending of the aluminum alloy is one of the processes frequently applied during manufacture of aircraft sheet metal. The bending operation involves springback, which is defined as elastic recovery of the part during unloading. In manufacturing industry, it is still a practical and difficult problem to predict the final geometry of the part after springback and to design appropriate tools in order to compensate for springback. In this study, 3D commercially available finite element analysis (FEA) software-MARC is used to analyse bending and springback of different aluminium materials (LY12CZ) with different thickness. The amount of springback, total equivalent plastic strains and equivalent von mises stresses are obtained. Moreover, the relation between bent angle and springback angle, R/t ratio and springback angle are presented and discussed in detail.The comparison results of FEA result and experiment data indicate that the FEM (finite element analysis method) simulation is a power tool for the highly accurate prediction of springback behavior in sheet metal bending.

Abstract: Two-axle rotary shaping is one of advanced sheet metal forming process that combined stamping ascendant used elastic medium with traditional rotary shaping principle. The prediction model of two-axle rotary shaping is set up to predict the springback for two-axle rotary shaping. It used the back propagation neural network because of the better nonlinear mapping ability. Some of data from the experiment and FEM simulation is applied to train the network; the other data is used to test the prediction result. The result showed that the value of prediction and experiment is in good agreement, and just small error is existed. It demonstrated that the neural network model might predict the springback of two-axle rotary shaping and reduce the number of simulation calculation and experiment operation. It can offer a powerful guidance for rapid choice of process parameters in production.