Correlation among the Power Dissipation Efficiency, Flow Stress Course, and Activation Energy Evolution in Cr-Mo Low-Alloyed Steel

Abstract

In the presented research, conventional hot processing maps superimposed over the flow stress maps or activation energy maps are utilized to study a correlation among the efficiency of power dissipation, flow stress, and activation energy evolution in the case of Cr-Mo low-alloyed steel. All maps have been assembled on the basis of two flow curve datasets. The experimental one is the result of series of uniaxial hot compression tests. The predicted one has been calculated on the basis of the subsequent approximation procedure via a well-adapted artificial neural network. It was found that both flow stress and activation energy evolution are capable of expressing changes in the studied steel caused by the hot compression deformation. A direct association with the course of power dissipation efficiency is then evident in the case of both. The connection of the presence of instability districts to the activation energy evolution, flow stress course, and power dissipation efficiency was discussed further. Based on the obtained findings it can be stated that the activation energy processing maps represent another tool for the finding of appropriate forming conditions and can be utilized as a support feature for the conventionally-used processing maps to extend their informative ability.

On the Mathematical Description of an Activation Energy Evolution

“…Similar flow-stress-like behavior has been observed e.g. in [7]. Nevertheless, the trends observed under lower temperatures are not in accordance with those observed under higher temperatures -Q-ε curves seem to be mirror-invertedthey show a global minimum.…”

“…In these equations, σ (MPa), ε (-), ε̇ (s −1 ) and T (K) represent a flow stress, true strain, strain rate and temperature, respectively. R (8.314 J⋅K −1 ⋅mol −1 ) is the universal gas constant, α(ε,T) (MPa −1 ) and α(ε) (MPa −1 ) are a strain-temperature dependent and only-strain dependent stress multiplier, respectively [7].…”

“…Based on the previous successful implementations of an artificial neural network approach [11,12] into the flow curve fitting process [7], a multi-layer feed-forward neural network [12] has been employed as an alternative to the above-mentioned multivariate polynomial approach. While an ideal network architecture was being found on the basis of a trial and error method, corresponding synaptic connections of artificial neurons were being calculated via the Levenberg-Marquardt algorithm [13,14] cooperating with the Bayesian regularization [15,16] under backpropagation of error signal [17].…”

Application of the Strain Compensation Model and Processing Maps for Description of Hot Deformation Behavior of Metastable β Titanium Alloy

“…Besides, one of the hot deformation optimization approaches for Ti-1023 alloy was the use of processing maps based on dynamic materials modeling (DMM) [17,18]. Superimposing hot processing maps over flow stress maps as well as overactivation of energy values to evaluate the hot workability of the investigated materials is quite often applied [19,20].…”

Hot Deformation Characteristics and Processing Parameter Optimization of Al–6.32Zn–2.10Mg Alloy Using Constitutive Equation and Processing Map

“…It is well known that the Z parameter is used to characterize the relationship between strain rate, deformation temperature and flow stress. Many studies have shown that the Z parameter can reflect the type of softening mechanism [33,34]. The lower lnZ value can promote the process of nucleation and growth of dynamic recrystallization (DRX) grains.…”