Abstract
Dynamic and meta-dynamic recrystallization occur during forging of alloy 718 aircraft parts and thus change the microstructure during a multistep production route. Since the prediction of the resulting grain structure in a single grain fraction is not able to describe microstructures with bimodal or even multimodal distributions, a multi-class grain size model has been deployed to describe the recrystallization mechanisms during thermomechanical treatments and predict the resulting grain size distributions more accurately. As forging parameters, such as temperature, strain rate and maximum strain influence the flow curve and consequently the recrystallization behavior, a series of double cone compression experiments has been carried out and used to verify and adapt the material parameters for the multi-class grain size model. The recrystallized fractions of the numerical and experimental results are compared and differentiated in view of the recrystallization mechanism, i.e., dynamic and meta-dynamic recrystallization. The strong dependence of the recrystallization kinetics on the initial grain size is highlighted, as well as the influence of different strain rates, which shall represent typical forging equipment.
Highlights
During the production of aircraft parts from alloy 718, the material undergoes several forging steps and heat treatments [1,2]
The focus is set to the separation of dynamic (DRX) and meta-dynamic (MDRX) recrystallization combined with their kinetics [3,22,23,24,25,26,27]
Using electron backscatter diffraction (EBSD), all Servotest samples forged at 980 ◦ C and 1020 ◦ C with subsequent holding times of 0 s, 5 s and 15 s were evaluated at 4 positions corresponding to strains of interest
Summary
During the production of aircraft parts from alloy 718, the material undergoes several forging steps and heat treatments [1,2]. In case of the forming equipment, such as screw presses and hydraulic or counterblow hammers, the difference in the energy input leads to difference in the recrystallization behavior of the material and leads to a variation in the microstructure of the final component [3,4,5,6,7,8,9,10,11,12,13,14]. An optimization of the process parameters to adjust the microstructural and mechanical properties is essential. For this reason, a digital model of the complete thermo-mechanical process sequence has been designed and experimentally validated to replicate the evolution of the microstructure [15,16,17]. The variation in the microstructure is based on simulations with industrially measured process data and is applied in a multi-class grain size model [28]
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