Effect of Supra-Transus Deformation Conditions on Recrystallization of Beta Ti Alloy

There is increasing usage of high strength Beta Ti alloy in aerospace components. However, one of the major challenges is to obtain homogeneous refined microstructures via the thermo-mechanical processing. To overcome this issue, an understanding of the hot deformation conditions effect on the microstructure, prior to and after annealing, is needed. In this work, the effect of strain levels, which is more precise than percentage of reduction, and strain rate under supra-transus deformation temperature on beta annealing are studied using a double cone sample. The Electron Backscattered Diffraction (EBSD) is used to determine the deformed microstructure and texture evolution, as well as the static recrystallized grains evolution using the ex situ annealing approach. This work provides evidence that the mechanisms of dynamic recovery and recrystallization, along with texture evolution, are affected by the deformation conditions, which in turn affected the subsequent static recrystallization during annealing. It will also be shown that high levels of strain do not necessarily lead to an increase in the rate of recrystallization. Finally, the results obtained provided several examples of guidance in designing the TMP processes for obtaining not only a refine microstructure, but a more homogeneous beta microstructure during the beta processing of Beta Ti alloy.

Development of thermal rolling regimes of low-alloy “ARC”-steel with quasi-homogeneous ferrite-bainitic structure

In this work, the kinetics of the growth of austenite grains upon heating, the features of the processes of dynamic and static recrystallization occurring at various temperature-deformation modes of plastic deformation are investigated. Phase transformations have been studied during continuous cooling of hotdeformed austenite in low-alloy “Arc”-steel with a yield point of at least 420 MPa. The studies carried out made it possible to determine the thermal deformation parameters that ensure the formation of a finely dispersed homogeneous ferrite-bainitic structure, on the basis of which technological recommendations for industrial production were developed and sheet products were manufactured. Presented are the structure and properties of sheet metal from shipbuilding “Arc”-strength category 420 MPa.

Scaling Scientific Cellular Automata Microstructure Evolution Model of Static Recrystallization toward Practical Industrial Calculations

An attempt to bridge the gap between capabilities offered by advanced full-field microstructure evolution models based on the cellular automata method and their practical applications to daily industrial technology design was the goal of the work. High-performance parallelization techniques applied to the cellular automata static recrystallization (CA-SRX) model were selected as a case study. Basic assumptions of the CA-SRX model and developed modifications allowing high-performance computing are presented within the paper. Particular attention is placed on the development of the parallel computation scheme allowing numerical simulations even for a large volume of material. The development of new approaches to handle communication within the distributed environment is also addressed in the paper as a means to obtain higher computational efficiency. Evaluation of model limits was based on the scalability analysis. The investigation was carried out for the 3D and 2D case studies. Therefore, the complex static recrystallization cellular automata simulation taking into account the influence of recovery, nucleation based on accumulated energy, and the progress of recrystallization as a function of stored energy and grain boundary mobility with high-performance computing capabilities is now possible. The research highlighted that parallelization is more effective with an increasing number of cellular automata cells processed during the entire simulation. It was also proven that the developed parallelization scheme and communication mechanism provides a possibility of obtaining scaled speedup over 700 times for 2D and over 800 times for 3D computational domains, which is crucial for future applications in industrial practice. Therefore, the presented approach’s main advantage is based on the possibility of running the calculation based on input data obtained directly from high-resolution 3D imaging of the microstructure. With that, the full immersion of the experimental results into the numerical model is possible. The second novelty aspect of this work is related to the identification of the quality of model predictions as a function of model size reductions.