Multipass forging of Inconel 718 in the delta-Supersolvus domain: assessing and modeling microstructure evolution

Abstract

Abstract. This work is focused on the evolution of the microstructure of Inconel 718 during multi-pass forging processes. During the forming process, the material is subjected to several physical phenomena such as workhardening, recovery, recrystallization and grain growth. In this work, transformation kinetics are modeled in the δ-Supersolvus domain (T>Tsolvus) where the alloy is single-phase, all the alloying elements being dissolved into the FCC matrix. Torsion tests were used to simulate the forging process and recrystallization kinetics was modeled using a discontinuous dynamic recrystallization (DDRX) two-site mean field model. The microstructure evolution under hot forging conditions is predicted in both dynamic and post-dynamic regimes based on the initial distribution of grain size and the evolution of dislocation density distribution during each step of the process. The model predicts recrystallization kinetics, recrystallized grain size distribution and stress-strain curve for different thermo-mechanical conditions and makes the connection between dynamic and post-dynamic regimes.

A new topological approach for the mean field modeling of dynamic recrystallization

“…with a "S" shape, see Fig. 9(a)), which is commonly observed in experimental investigations of DRX [38,39]. Furthermore, evolutions of recrystallized fraction, mean grain size weighted 350 by volume and dislocation density weighted by volume described with the NHM are quite close to those described by the full field model.…”

In Situ Characterization of Inconel 718 Post-Dynamic Recrystallization within a Scanning Electron Microscope

“…The influence of the thermomechanical forging conditions on dynamic recrystallization (DRX) kinetics and on the DRX grain size is relatively well established [1][2][3][4][5][6][7][8], and can even be predicted using either physical or empirical models [9][10][11][12][13][14][15]. However, previous studies have shown that post-dynamic evolutions may also considerably impact the final microstructure [12,[15][16][17][18]. The post-dynamic regime encompasses everything that occurs right after deformation stops, while the material is still at high temperature.…”

“…Microstructure evolution within the post-dynamic regime can be extremely fast, especially when prior deformation is performed at high strain rates [12,15,17]; microstructure can be fully regenerated within only a few to a few tens of seconds. Higher strain rates, higher temperature and/or higher levels of prior strain have been reported as accelerating factors for post-dynamic microstructure evolution [18,22].…”

Dynamic recrystallisation model in precipitation-hardened superalloys as a tool for the joint design of alloys and forming processes

“…This may be achievable if a model is available, capable of predicting DRX features as a function of alloy compositionand hence phase constitution -as well as of processing conditions (temperature, strain rate, strain). Most models, however, provide a description of microstructural evolution using fitted parameters and are not predictive regarding the influence of composition [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. For instance, some authors [8][9][10][11][12][13][14] have used parameterised equations, mostly relying on or deriving from the Zener-Hollomon parameter, to describe the high temperature flow stress of different superalloys, the dependence of grain size on temperature, strain rate, and/or the evolution of the recrystallised fraction with strain.…”

“…size and dislocation density) of one or several specific grains, and on their interaction with their surroundings. The latter is considered as a continuous medium with averaged properties [20], or as a homogenised medium made of fractions of recrystallised and non-recrystallised grains having different properties [21]. Calculating the evolution of individual grains can also be made without a mean field approach, by simulating a large and representative set of grains by a cellular automaton [22][23][24].…”