A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel

Ling Qiao,Yuan Wang,Yong Deng,Jingchuan Zhu

doi:10.1109/access.2020.2986389

Abstract

In order to predict the high-temperature deformation behavior of hypereutectoid steel, the hot compression tests were conducted in the strain rate range of (0.001~1) s -1 and the deformation temperature range of (950~1100)°C. The experimental data were employed to develop the Arrhenius constitutive model and BP neural network model, and their predictability for high temperature flow stress of hypereutectoid steel was further evaluated. Comparatively, a higher correlation coefficient (R) can be obtained for the BP neural network model compared with the Arrhenius constitutive equations. And the relative error within ±1% was more than 68.75% for the BP neural network model, while only 18.75% for the constitutive equations. The BP neural network model is considered more efficient and accurate to predict the hot deformation behavior than the Arrhenius constitutive equations. Moreover, the well-trained BP neural network model is employed to predict the flow stress varying with the deformation temperature and strain rate. The flow stress decreases with the increasing deformation temperature and decreasing strain rate, which is in accordance with the experimental evaluation. The results indicate that BP neural network model is an efficient tool for modelling and predicting the flow behavior of hypereutectoid steels in high temperature applications.

Highlights

Hypereutectoid steel is widely applied in many industrial fields for its great mechanical property, especially in the field of rail technology [1]–[3]
The flow stress tends to be intensely dependent on the forming temperature and strain rate under the working conditions
It is found that the flow stress decreases with the increase in deformation temperatures for a given strain rate

Summary

Introduction

Hypereutectoid steel is widely applied in many industrial fields for its great mechanical property, especially in the field of rail technology [1]–[3]. The understanding of the flow behaviors of steels has become more essential to researchers engaged in hot deformation condition. Knowledge of the hot deformation mechanisms is considered to be an essential prerequisite for the optimization of material properties [4]. The constitutive model is considered to be a great analytic method to describe the hot deformation behavior of materials based on the experimental data. Without special considerations on the essential physical process, the Arrhenius type model was successfully used to describe the relationships between the mechanical properties and hot working conditions mathematically [6]–[8]. Wu et al [9] proposed that the constitutive equation can be established to understand the thermal deformation mechanism and optimizing the thermal processing parameters and leading to improved thermal processing properties of steels.

Methods

Results

Conclusion