Previsão do crescimento de grão por elementos finitos a partir da recristalização dinâmica em um processo de laminação a quente de liga de aço SAE 4140
Durello, Henrique Augusto Camargo
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In the present study, we analyzed the influence of the parameters relevant to the Avrami model to describe the dynamic recrystallization kinetics and the final size of the recrystallized grains, in addition to their distribution on a hot-rolled plate of steel alloy SAE 4140. is analyzed by Finite Elements (EF) in the commercial program DEFORM 3D for the purpose of determining the stress, strain and temperature fields. Avrami's model is post-processed from the EF results, together with material-specific data and general numerical modeling aspects taken as a reference from what is obtained in the literature. The study is applied to only one lamination step, aiming to reduce manufacturing time and costs. Some representative points of the plate were chosen for the purpose of the analyses. Discussions based on the Fe-C alloy phase diagram and TTT diagram (time, temperature and transformation) were presented in order to provide an interpretation of the EF model related to dynamic recrystallization effects. For the conditions studied, the grains showed final sizes after recrystallization in the order of 2.55 to 36.5 μm with changes in variables. After 15 seconds of rolling simulation, the influence of the initial temperature of the sheet indicated an increase in the recrystallized grain size, but in the case of sheet cooling (convective coefficient) this final grain size was approximately constant. With the reduction in thickness and increase in the deformation rate of the plate, a reduction in the recrystallized grain size was observed. Guidance was presented on the conditions that provide an increase in grain size at the end of dynamic recrystallization. Conversely, it is possible to extract from this practical guideline for the minimization of grain size in the process. The results presented lack a greater experimental basis, which opens the possibility of forming a wide collection of data from the studied process and that serve as a basis for mathematical adjustments to automatic algorithms that can be used in an agile way on the factory floor.
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