Microstructure Prediction Of316LN Strainless Steel For Dynamic Recrystallization Based On Cellular Automata Method

Nowadays，AP1000nuclear power technology was the safest and most advancedcommercial nuclear power technology. Its unit main forging selects316LN extra-lowcarbon austenite stainless steel. which has high organizational performance requirementsand grain fineness of forging. The organization of316LN is single phase austenitic, andthe grain refinement is almost entirely dependent on the dynamic recrystallization of hotdeformation process. Studying evolution of grain structure of316LN in hot deformationhad great significance to design forging process of main pipes and control the finalproperties of products.According to thermo-mechanical simulation and metallographic experiment ondifferent conditions, mathematical model is established in forecast the hot working processof organization is used common in researching, but it cannot describe the complex of grainevolution in the process of dynamic recrystallization. Cellular automata(CA) are a kind ofkinetic model, which are according to the evolution of local rules in the cellular spaceswhich consist of time, space and cellular. The algorithm structure of CA is simple andcomputationally efficient. CA can be used for many kinds of simulation of nonlinearphysical process. In this paper, based on the physical theory of thermal deformation formetal, experimental results and dislocation density, macro-deformation process andmicrostructure evolution were contacted, normal grain growth and the CA model ofdynamic recrystallization were established It also combined FEM to realize the simulationand forecast of microstructure evolution of forging for316LN.In this paper, though the hot compress experiment done on the Gleeble-3500thermomechanical simulator, the stress-strain curve at deformation temperature of900~1200oCand strain rate of0.001~10s-1was obtained. Combined with metallographic experiment,the effects of deformation parameter on flow stress of316LN and microstructure evolutionwas researched, the model of grain size in static recrystallisation were established.According to the relationship between stress and dislocation and kinetic equation ofdynamic recrystallization, the flow stress model of316LN at high temperature was established, and material parameter of CA model was obtained.Based on the mechanisms such as thermal activation, curvature-driven, energydissipation, it had come to cellular automata inference rules. And by adding the largestdecline criterion of energy, it had established the cellular automata model of316LNstainless steel. The model described growth of grain during heat treatment. By thesimulation of316LN grain growth conditions at1000~1100oC. It obtained desired initialmicrostructure in dynamic recrystallization of cellular automata simulation. Therelationship between actual time and simulation time was established, and the predictionof grain size of316LN in the reheating process was also realized.Based on the condition of nucleation of dislocation-driven and the grain growthdynamics theory, the equation of nucleation rate was corrected, and the CA model ofdynamic recrystallization of316LN was established. This paper simulated the process ofmicrostructure evolution of dynamic recrystallization on different parameters, verifiedpredictive capability of CA model on characteristics of dynamic recrystallisation, anddeveloped simulation system of predicting microstructure evolution about316LN stainlesssteel during dynamic recrystallization independently.In order to validate the application effect of CA model of dynamic recrystallizationfor316LN, this paper combined FEM to make a new rule of thermal distortion parameter,introduced strain rate and strain obtained from DEFORM-3D simulation to Cellularautomata (CA) model, and simulate microstructure evolution of actual forging process.The simulated results of the volume fraction of dynamic recrystallization, stationary grainsize and microstructure evolution process were the same to the test results.The results showed that the model established in this paper can accurately simulate316LN grain growth in heat treatment and the microstructure evolution of the dynamicrecrystallization during hot deformation, simulation results meet the relevant theories andexperimental results of metallographic. It can achieve316LN tissue simulating and grainforecasts of dynamic recrystallization in forging process.