No abstract
Nonlinear unloading plays an important role in predicting springback during plastic forming process. To improve the accuracy of springback prediction which could provide a guide for precision forming, uniaxial tensile tests and uniaxial loading–unloading–loading tensile tests on SUS304 stainless steel were carried out. The flow stress mathematical model and chord modulus mathematical model were calibrated according to the test results. A constant elastic modulus three-point bending finite element model (E0FEMB) and a constant elastic modulus roll forming finite element model (E0FEMR) were established in MSC.MARC. The chord modulus was output by the PLOTV subroutine to determine the mean modulus of different regions, and the mean modulus three-point bending finite element model (E¯cFEMB) and the mean modulus roll forming finite element model (E¯cFEMR) were defined. The constant modulus finite element model (E0FEM) simulation results and the mean modulus finite element model (E¯cFEM) simulation results were compared with the three-point bending tests and roll forming tests test results. The difference between the simulation results and the test results was small, indicating that the mean modulus was feasible to predict the springback, which verified the suitability of the E¯cFEM.
Taking 20CrMn steel as the experimental object, the vibration and non-vibration cast-rolling comparison experiments were carried out on the self-developed twin-roll strip vibration cast-rolling mill. And a series of tests such as tensile test, fracture morphology observation and energy spectrum analysis were carried out on the obtained cast-rolling strips. This paper focuses on the analysis of the test results from the perspective of the second phase particles, in order to explore the effect of vibration on the precipitation of the second phase particles in cast-rolling. The analysis results show that the vibration can promote the uniform distribution of alloying elements, thereby inhibiting the formation of large size second phase particles which are mainly concentrated in the central region of the strip in the late solidification stage, and promoting the precipitation of the dispersed small-sized particles in the high-temperature plastic deformation stage, and finally improving the mechanical properties of the cast-rolling strip. At the end of the paper, the influence of vibration parameters such as vibration amplitude and vibration frequency on the formation of second phase particles is further analyzed.
The outer surface of the tube is worn under the interaction between the velocity difference and rolling pressure, in the process of rolling the circular tube into a rectangular tube. In order to predict the wear depth, according to the characteristics of roll forming, the causes of wear in the forming process are analyzed. The finite element model of rolling forming was established based on Archard theory, and the 40 mm × 27.5 mm × 3 mm SUS304 stainless steel rectangular tube was simulated. The simulated results were compared with a test rolling of the steel tubes of the same size material, and the wear areas were found to be highly consistent, which verified the accuracy of the finite element model. The effects of the friction coefficient and the flat roller angular velocity on the simulation results which wear depth were analyzed, and the regression model of wear depth was established by response surface method. The results showed that the flat roller angular velocity had the greatest effect on wear depth; moreover, the flat roller friction coefficient was the second, and the vertical roller friction coefficient was the lowest. The minimum value of the regression model was optimized, the simulation value of the optimization scheme was compared with the optimized value, and the error of the two values was less than 5%, which verified the correctness of the regression model. The wear depth of the rectangular tube after optimization was reduced by 64.69% compared with that before optimization, which verified the effectiveness of the optimization results.
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