Metadynamic Recrystallization Behavior of Cr-Ni-Mo Alloy Steel

Author:

Zheng Bing123,Zou Zhipeng34,Xu Dong23ORCID,Wang Yiqun2,Wang Xuexi2,Zhao Hongyang1,Ju Dongying1

Affiliation:

1. School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan 114051, China

2. Technology Center, Henan Zhongyuan Special Steel Equipment Manufacturing Co., Ltd., Jiyuan 459008, China

3. Technology Innovation Center for High Quality Cold Heading Steel of Hebei Province, Hebei University of Engineering, Handan 056038, China

4. Technology Innovation Center for Wear Resistant Steel Plate of High Plasticity and High Toughness of Hebei Province, Hebei Puyang Iron and Steel Co., Ltd., Handan 056305, China

Abstract

In order to study the metadynamic recrystallization behavior of 34CrNi3MoV steel, a double-pass isothermal compression experiment and a single-pass thermal interval experiment were designed and conducted to obtain the stress–strain curves under different deformation conditions and to explore the action law of deformation parameters during the compression process. The softening rate was calculated by the compensation method, and the grain size in the recrystallization region was measured. Based on the obtained data, the effects of deformation temperature (T), interval time (t), and strain rate (ε˙) on the softening rate and grain size of 34CrNi3MoV steel during metadynamic recrystallization were analyzed. The results show that increasing the deformation temperature, extending the interval time, and increasing the strain rate are all beneficial to the improvement of the metadynamic recrystallization softening rate and that fine and uniform new grains can be obtained under a high strain rate. However, in high-temperature conditions, mixed crystallization can easily occur, which is not conducive to grain refinement. Based on the true stress–strain data and experimental data on the grain size, a relevant model for metadynamic recrystallization of 34CrNi3MoV steel was established using mathematical analysis of regression equations. The average relative error AARE between the constructed dynamic model and the grain size model and the experimental results are 6.48% and 1.30%, respectively. This indicates that the model has high predictability.

Funder

National Natural Science Foundation of China

Special Projects for Military–Civilian Collaborative Innovation in Science and Technology of Hebei Province

a key project of the Handan Scientific Research Program

Science and Technology Project of the Hebei Education Department

Publisher

MDPI AG

Subject

General Materials Science,Metals and Alloys

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