Investigation of flow stress behavior of AISI 4340 steel in thermomechanical process

In this study, flow stress behavior of AISI 4340 steel in thermomechanical process was investigated under temperature and strain rate ranges of 1173 to 1373 K and 0.01 to 1 s-1, respectively. In flow curves, mechanisms such as work hardening (WH), dynamic recovery (DRV) and dynamic recrystallization (DRX) occurred. It was also discovered that the flow stress decreases with the increase of deformation temperature and the decrease of strain rate. Flow stress curves declared that in low-strain rate and high temperature, dynamic recrystallization overcome work hardening. Also, decreasing temperature led to dynamic recovery and incomplete dynamic recrystallization. Work hardening rate-stress curves depicted that the presence of a turning point expresses dynamic recrystallization mechanism and sub-boundaries are formed at the beginning of where a turning point occurs. In partial dynamic recrystallization, the microstructure was consisted of long grains reshaped because of deformation and some recrystallized grains that nucleated around those reshaped long grains. The results also indicated that at temperature of 1373 K, stress value of σsf, for strain rate of 0.01 s-1 was increased from 27.8 MPa to 96.5 MPa and also for strain rate of 1 s-1 and stress of σc was increased from 32.3 MPa to 105 MPa. The significance of the approach used in this work was any increase in strain rate leads to accelerating dislocation movements. Therefore, dislocations will hit the barriers sooner and will be stopped and also, as a result of delayed dynamic recovery due to dislocations movements, dynamic recrystallization is also delayed

Thermomechanical process modelling of 40NICRMO8-4 alloy by artificial neural networks

Artificial neural networks (ANNs) as simplified model of mankind’s neural system, are capable of simulating and predicting real world complex problems which are challenging and expensive to model physically. In this study the correlation between the flow stresses and strain rate, temperature, strain in thermomechanical process of 40NICRMO8-4 alloy has been modelled. The results revealed that flow stress for every strain value is less at high temperatures compared to those at low temperatures and material resistance against deformation will also decrease as temperature goes down. Moreover, increasing in strain rate when temperature is constant results in recrystallization to happen in higher strain values at times shorter. The employed neural network for this study was a feed forward multilayer perceptron trained with common back propagation algorithm. Similar to any other ANNs, the employed network receives some parameters as inputs and delivers some as outputs. The inputs given to this model were temperature, strain and strain rate while flow stress parameter was collected as requested output. Outputs, with high precision of approximately 99% accuracy, were predicted and produced during training phase. Likewise, the predicted output of the ANN model achieved an R-value of about 0.99871 compared with of those experimental values. Best results were obtained with an ANN model consist of two hidden layers trained with Levenberg–Marquardt training algorithm