Abstract
The high-temperature deformation behavior of metals and alloys undergoes complex mechanisms depending on the deformation conditions. The microstructure and mechanical properties after deformation are important factors that determine the strength and durability of the final product. Therefore, many studies to predict the microstructure and mechanical properties have been conducted. In this regard, numerous mathematical approaches for predicting microstructure and flow stress have been proposed over the past half century. Accordingly, many advances have been made in the field of material science. Nevertheless, there are limitations in the mathematical modeling method as there is a complex relationship between the deformation conditions and the mechanical properties. Therefore, in this study, flow stress prediction was performed by applying conventional constitutive equation and artificial intelligence technology, which is known to be effective in modeling complex relationships. As a result, it was confirmed that the flow stresses modeled by the artificial neural network showed a higher accuracy than the flow stresses modeled by the conventional Arrhenius hyperbolic sine equation.
Highlights
Academic Editor: Andrea Di SchinoThe deformation behavior of metals and alloys in the high-temperature forming process is difficult to predict because they are affected by very complex mechanisms [1].understanding and predicting microstructural changes are a major concern in the field of materials science and engineering because microstructure control is an important factor in improving the mechanical properties of final products
It can be seen that the flow stresses tended to decrease as the temperature increased and the strain rate decreased, which are general high-temperature deformation characteristics
On the other hand, when the temperature was high or the strain rate was low, secondary work hardening in which the stress increased again after the steady state was observed
Summary
The deformation behavior of metals and alloys in the high-temperature forming process is difficult to predict because they are affected by very complex mechanisms [1]. It is well known that the high-temperature deformation of metals is governed by work hardening, dynamic recrystallization (DRX), and dynamic recovery (DRV) [2,3,4]. Understanding the above three mechanisms is an important factor for predicting high-temperature deformation behavior, and many studies have been conducted for this purpose [6,7,8,9,10]. It was reported that the reason the ANN approach is more effective is because the constitutive equation considers only the DRX phenomenon, so the effects of twin formation and grain growth that occur in actual deformation are excluded [20]. ANN shows a more accurate performance for solving nonlinear and complex problems
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