In order to simulate and analyze hot strip crown and flatness accurately and efficiently, the 3-D （three-di- mensional） coupled model involved in RPFEM （rigid-plastic finite element method） is improved based on th...In order to simulate and analyze hot strip crown and flatness accurately and efficiently, the 3-D （three-di- mensional） coupled model involved in RPFEM （rigid-plastic finite element method） is improved based on the analyti- cal model of forecasting rolling force distribution. In the analytical model, variational method is employed to solve the lateral flow of metal and influential function method is employed to calculate roll deflection, the lateral distribution of rolling force can be obtained rapidly by iterative strategy. Then the 3-D coupled model uses the result as initial distri- bution of rolling force to calculate roll deflection and makes the initial on-load roll gap profile close to the final value, so as to reduce iterations and increase efficiency. Compared with previous algorithms, the improved model can reduce the iterations by about 50% and shorten the computing time by about 60% on the basis of the calculation accuracy.展开更多
基金Sponsored by National Natural Science Foundation of China (51075353)Hebei Natural Science Foundation of China (E2010001208)
文摘In order to simulate and analyze hot strip crown and flatness accurately and efficiently, the 3-D （three-di- mensional） coupled model involved in RPFEM （rigid-plastic finite element method） is improved based on the analyti- cal model of forecasting rolling force distribution. In the analytical model, variational method is employed to solve the lateral flow of metal and influential function method is employed to calculate roll deflection, the lateral distribution of rolling force can be obtained rapidly by iterative strategy. Then the 3-D coupled model uses the result as initial distri- bution of rolling force to calculate roll deflection and makes the initial on-load roll gap profile close to the final value, so as to reduce iterations and increase efficiency. Compared with previous algorithms, the improved model can reduce the iterations by about 50% and shorten the computing time by about 60% on the basis of the calculation accuracy.
