Based on twin-roll casting, a cast-rolling force model was proposed to predict the rolling force in the bimetal solid-liquid cast-rolling bonding(SLCRB) process. The solid-liquid bonding zone was assumed to be below t...Based on twin-roll casting, a cast-rolling force model was proposed to predict the rolling force in the bimetal solid-liquid cast-rolling bonding(SLCRB) process. The solid-liquid bonding zone was assumed to be below the kiss point(KP). The deformation resistance of the liquid zone was ignored. Then, the calculation model was derived. A 2D thermal-flow coupled simulation was established to provide a basis for the parameters in the model, and then the rolling forces of the Cu/Al clad strip at different rolling speeds were calculated. Meanwhile, through measurement experiments, the accuracy of the model was verified. The influence of the rolling speed, the substrate strip thickness, and the material on the rolling force was obtained. The results indicate that the rolling force decreases with the increase of the rolling speed and increases with the increase of the thickness and thermal conductivity of the substrate strip. The rolling force is closely related to the KP height. Therefore, the formulation of reasonable process parameters to control the KP height is of great significance to the stability of cast-rolling forming.展开更多
Coupled turbulent flow, temperature fields of the twin-roll casting strip process were simulated by three-dimensional finite element method. Based on the heat balance calculation and using inverse methods between the ...Coupled turbulent flow, temperature fields of the twin-roll casting strip process were simulated by three-dimensional finite element method. Based on the heat balance calculation and using inverse methods between the simulations and real experiments, the relational models among casting speed, location, and coefficient of heat transfer between molten metal and rolls in different regions are given. In the simulation, the calculated surface temperatures are in good agreement with the measured values. An on-line model of kiss point is derived by simulations and the geometry of molten pool, corresponding control strategy is also proposed.展开更多
Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting str...Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of ±7.0%,which indicates that the predicting accuracy of this model is very high.展开更多
基金The authors are grateful for the financial supports from the National Natural Science Foundation of China(51974278)the Distinguished Young Fund of Natural Science Foundation of Hebei Province,China(E2018203446).
文摘Based on twin-roll casting, a cast-rolling force model was proposed to predict the rolling force in the bimetal solid-liquid cast-rolling bonding(SLCRB) process. The solid-liquid bonding zone was assumed to be below the kiss point(KP). The deformation resistance of the liquid zone was ignored. Then, the calculation model was derived. A 2D thermal-flow coupled simulation was established to provide a basis for the parameters in the model, and then the rolling forces of the Cu/Al clad strip at different rolling speeds were calculated. Meanwhile, through measurement experiments, the accuracy of the model was verified. The influence of the rolling speed, the substrate strip thickness, and the material on the rolling force was obtained. The results indicate that the rolling force decreases with the increase of the rolling speed and increases with the increase of the thickness and thermal conductivity of the substrate strip. The rolling force is closely related to the KP height. Therefore, the formulation of reasonable process parameters to control the KP height is of great significance to the stability of cast-rolling forming.
基金supported by National Key Research Development Planning Project of China (2004CB619108).
文摘Coupled turbulent flow, temperature fields of the twin-roll casting strip process were simulated by three-dimensional finite element method. Based on the heat balance calculation and using inverse methods between the simulations and real experiments, the relational models among casting speed, location, and coefficient of heat transfer between molten metal and rolls in different regions are given. In the simulation, the calculated surface temperatures are in good agreement with the measured values. An on-line model of kiss point is derived by simulations and the geometry of molten pool, corresponding control strategy is also proposed.
基金Project(2004CB619108) supported by National Basic Research Program of China
文摘Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of ±7.0%,which indicates that the predicting accuracy of this model is very high.
