A characterization for the constitutive relationships of 42CrMo high strength steel by Artificial Neural Network and its application in isothermal deformation

Abstract

On hot working process, the prediction of material constitutive relationship can improve the optimization design process. Recently, the artificial neural network models are considered as a powerful tool to describe the elevated temperature deformation behavior of materials. Based on the experimental data from the isothermal compressions of 42CrMo high strength steel, an artificial neural network (ANN) was trained with standard back-propagation learning algorithm to predict the elevated temperature deformation behavior of 42CrMo steel. The inputs of the ANN model are strain, strain rate and temperature, whereas flow stress is the output. According to the predicted and experimental results, it indicates that the developed ANN model shows a good capacity of modeling complex hot deformation behavior and can accurately tracks the experimental data in a wide temperature range and strain rate range. On addition, the predicted data outside of experimental conditions were obtained, indicating good prediction potentiality of the developed ANN model. The q -s curves outside of experimental conditions indicate that the predicted strain-stress curves exhibit a typical dynamic recrystallization softening characteristic of high temperature deformation behavior. Through the coupling of the ANN model and finite element model, the hot compression simulations at the temperature of 1273 K and strain rates of 0.01~10 s -1 were conducted. The results show that the predicted constitutive data outside the experimental conditions successfully improved the prediction accuracy of forming load during the FEM simulation.

New constitutive model for high-temperature deformation behavior of inconel 718 superalloy

“…However, for the dynamic softening period, there are relatively large deviations under the deformation temperature of 1040°C and high strain rates. The previous investigations [54,64] indicate that d phase (Ni 3 Nb) in the Ni-based superalloy starts to dissolve when the temperature is higher than 980°C and completely dissolves when the temperature is higher than 1038°C. Therefore, the relatively large deviations between the predicted and experimental results are induced by the complete dissolution of d phase under 1040°C.…”

“…Lin and Chen [1] presented a critical review on the development of constitutive descriptions for metals and alloys under hot working in recent years, and the constitutive models are divided into three categories, including the phenomenological , physically-based [47][48][49][50][51][52][53][54][55] and artificial neural network models [56][57][58][59][60]. Considering the effects of strain on material constants, Lin et al [18] proposed a modified Arrhenius model to describe the hot deformation behavior of 42CrMo steel at elevated temperatures by the compensation of strain and strain rate.…”

“…Based on the classical stress-dislocation relation and the kinetics of dynamic recrystallization, the constitutive equations were established to describe the work hardening, dynamic recovery and dynamical recrystallization behavior of 42CrMo steel [48], TiAl alloy [49,50], commercial purity (CP) titanium [51], 4340 steel [52], and a typical nickel-based superalloy (GH4169) [53]. Additionally, neural network models were developed to predict the flow stresses of alloy steels [54,55], titanium alloys [56,57], and aluminum alloys [58,59]. Besides, some internal variable thermo-mechanical constitutive models have been proposed or modified in recent years.…”

Determining the Hot Workability and Microstructural Evolution of an Fe-Cr-Mo-Mn Steel Using 3D Processing Maps

“…Li [ 18 ] constructed a hyperbolic sine-type equation based on the Zener–Hollomon ( Z ) parameter. Quan [ 19 ] predicted the high-temperature deformation behavior of 42CrMo by utilizing the back propagation learning algorithm of an artificial neural network. Lin [ 20 ] constructed the flow stress constitutive equations by using the hyperbolic sine function.…”

A Characterization of Hot Flow Behaviors of Invar36 Alloy by an Artificial Neural Network with Back-Propagation Algorithm

“…As the well-trained ANN model could provide a wide range of flow stress data, it can be applied in numerical simulation with high accuracy. Quan et al developed an ANN model for 42CrMo high strength steel and improved the accuracy of finite element method (FEM) simulation by importing a wide range of stress-strain data predicted by the ANN model 14 . In current work, the ANN model for Invar36 alloy was successfully applied to numerical simulation by using FEM on DEFORM-3D software, and the dependability of finite element simulation based on stress-strain data predicted by ANN model has been demonstrated through a hot forming experiment of a V-shaped part.…”