Jingyu Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jingyu Zhang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
Guiming Yang
Foshan Shunde Xinhuanbao Resource Utilization Co., Ltd. Foshan 528399 China
1 shared publicationHongjun Liu
School of Material Science and Engineering Lanzhou University of Technology Lanzhou 730050 China
1 shared publicationJiayan Ji
School of Material Science and Engineering Lanzhou University of Technology Lanzhou 730050 China
1 shared publicationQian Du
School of Material Science and Engineering Lanzhou University of Technology Lanzhou 730050 China
1 shared publicationQinrui Zhang
School of Material Science and Engineering Lanzhou University of Technology Lanzhou 730050 China
1 shared publication- Yi Meng
College of Materials Science and Engineering Chongqing University Chongqing 400044 China
1 shared publication
Latest Publications
- 13 October 2025
- Published by Wiley in Steel Research International
- Vol. 97 (4) , 2190-2207
Study on the Thermal Deformation Behavior of Cr–Mn–Si High‐Strength Steel and Determination of Grain Refining Parameter Pathways
In order to study the deformation behavior of 30CrMnSiNi2A high‐strength steel during thermal forming, a series of hot compression experiments are conducted under deformation conditions in the temperature range of 850–1200 °C and true strain rates of 0.1, 0.5, 1, and 10 s −1 . Based on the experimental data, the modified Johnson–Cook equation is calculated, and then the backpropagation artificial neural network constitutive model (BP‐ANN) is obtained. By comparison, the BP‐ANN has obvious advantages in describing and predicting the flow behavior of the material. The thermal processing map is drawn to delineate the three regions of dynamic recrystallization, dynamic recovery, and instability region, which is combined with the microstructure map to determine the critical value dominated by the occurrence of dynamic recrystallization mechanism and to construct the deformation mechanism map. At a fixed temperature, finite element simulations are carried out sequentially for different strain stages to determine the average grain size of each stage and determine the optimal parameter loading path. It helps to realize the effective control of the microstructure of the material, to obtain the ideal grain refinement effect.
- 13 October 2025
- Published by Wiley in Steel Research International
- Vol. 97 (4) , 2190-2207
Study on the Thermal Deformation Behavior of Cr–Mn–Si High‐Strength Steel and Determination of Grain Refining Parameter Pathways
In order to study the deformation behavior of 30CrMnSiNi2A high‐strength steel during thermal forming, a series of hot compression experiments are conducted under deformation conditions in the temperature range of 850–1200 °C and true strain rates of 0.1, 0.5, 1, and 10 s −1 . Based on the experimental data, the modified Johnson–Cook equation is calculated, and then the backpropagation artificial neural network constitutive model (BP‐ANN) is obtained. By comparison, the BP‐ANN has obvious advantages in describing and predicting the flow behavior of the material. The thermal processing map is drawn to delineate the three regions of dynamic recrystallization, dynamic recovery, and instability region, which is combined with the microstructure map to determine the critical value dominated by the occurrence of dynamic recrystallization mechanism and to construct the deformation mechanism map. At a fixed temperature, finite element simulations are carried out sequentially for different strain stages to determine the average grain size of each stage and determine the optimal parameter loading path. It helps to realize the effective control of the microstructure of the material, to obtain the ideal grain refinement effect.