Bending and drawing are widely used in sheet metal forming field. They are very suitable for the mass production. In order to avoid large-scale part defects in the productive process, it is necessary to make a deeply study on bending and drawing process. In this paper, bending and drawing processes were treated as research object, then, the inverse determination of bending material parameter, the prediction of bending springback, the stress analysis on secondary deep drawing process of cup-shaped part and the prediction of cup shaped part forming quality were studied, and a sheet metal forming prediction system suitable for the integration of numerical control equipment was developed. This paper provides the necessary algorithm and technical supports for numerical control bending and forming equipments, which has great significances to develop related products.
A dynamic approximation optimization approach was proposed by the radial basis function and particle swarm optimization. In comparison with static approximation optimization method, the dynamic approximation optimization approach has the advantages of less labor consumption and higher calculated accuracy. Using the dynamic approximation optimization approach, based on the combination of bending experimental and finite element simulated results, the plastic parameters of B340LA steel plate were inverse determined. The inverse determined material parameters show better accuracy in comparison with the actual one. It is an efficient and simple way to obtain the material plastic parameters as the inverse determination approach of this paper.
In order to obtain the bending angle prediction model similar to the DELEM system, the bending springback compensated algorithm was researched, and a practical bending springback angle calculation formula was deduced out. Due to the existence of assumption and empirical formula, the deviation between calculation formula and DELEM system was larger. Some parameters in the empirical formula of calculation formula were inverse determined based on the test data of DELEM system. According to the curve expression of the inverse determined parameters, the corresponding empirical formula was obtained. Then, the calculation formula was improved when replace the empirical formula with the new one. The improved calculation formula has achieved the precision level of DELEM system basically.
Considering the strong data fitting ability of error feedback neural network (BPNN), a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline) is presented. An orthogonal experimental sample set for training BPNN-Spline is obtained by precision finite element simulation, and the BPNN-Spline black box function of bending angle prediction is established. The BPNN-Spline has advantages on two aspects: accurate BPNN training results and monotonically decreasing relation between bending angle and punch displacement are easier to guarantee. The application range of BPNN-Spline model was extended, because of the independent relationship between bending angle and length zooming factor, then, the versatility of the model was enhanced. The BPNN-Spline model shows higher prediction accuracy under various bending working conditions.
Because the theoretical research of secondary deep drawing process is serious shortage, a stress analysis model for the secondary deep drawing process of a cup-shaped part was represented in this paper. The radial stress analysis model taking the punch displacement as the independent variable was obtained. Then, the punch force calculation method was obtained. Through the comparison with finite element simulated and experimental results, the calculation model shows better calculated accuracy, only a little deviation was occurred at some special locations. The deviation was caused by the more idealized analysis model which is incomplete agreement with the actual secondary deep drawing process. Through the impact analysis of forming parameters, it can be observed that the maximum punch force will be decreased by the decreasing initial blank radius, increasing cup-shaped straight wall radius, round corner radius of blank holder and round corner radius of die.
In order to rapidly and accurately predict forming quality of cup-shaped part under diverse material and working condition, a BPNN-Spline forming quality prediction model was proposed in this paper. This prediction model is very suitable to be applied in the numerical control equipment as a control algorithm, due to its better versatility, higher prediction precision and faster calculation speed. The model shows better prediction ability in comparison with the finite element simulated and experimental test examples. Taking the prediction model as the solver, an optimization approach of cup-shaped part process parameters was proposed. Through the test of optimization example, the optimization approach shows higher optimization efficiency and precision.
In order to the users conveniently use the prediction models of bending and forming, a sheet metal forming prediction system was developed. The current version of prediction system can predict the bending angle, predict the cup-shaped part forming quality, optimize the cup-shaped part process parameters, and so on. The sheet metal forming prediction system can implement the prediction calculation by calling the MATLAB function program. It is beneficial to the later maintenance work of system due to the independent relationship between the application interface and calculation kernel, and it has great significances to promote the efficiency and technical level of related enterprises.