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Microstructure prediction of multi-directional forging for 30Cr2Ni4MoV steel by the secondary development of Deform software and BP neural network

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Abstract

The true stress–strain curve of the thermal deformation of 30Cr2Ni4MoV steel was obtained by conducting a hot compression experiment. On the basis of the results, the constitutive equation of the material’s thermal deformation was constructed. Then, the microstructure of the hot compression specimen was observed and analyzed, and the microstructure evolution model of 30Cr2Ni4MoV steel during thermal deformation was established accordingly. The programming of the related mathematical model was realized using the secondary development interface provided by Deform. The experimental plan was subsequently developed using an orthogonal method, and the microstructure evolution of 30Cr2Ni4MoV steel during multi-directional forging deformation was simulated. Through an orthogonal experiment analysis, the influence weight difference of each influencing factor was obtained. A back propagation neural network prediction model for the microstructure of 30Cr2Ni4MoV steel under multi-directional forging deformation was then established. The neural network prediction of the microstructure evolution of 30Cr2Ni4MoV steel under multi-directional forging was finally realized.

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Availability of data and material

The datasets used or analyzed during the current study are available from the authors on reasonable request.

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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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Funding

This project is supported by the Natural Science Foundation of Hebei Province, China (Grant No. E2019203005).

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Authors and Affiliations

  1. Education Ministry Key Laboratory of Advanced Forging & Stamping Technology and Science, Yanshan University, Qinhuangdao, 066004, China

    Junting Luo, Jingqi Zhao, Zheyi Yang & Yongbo Jin

  2. State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Junting Luo & Chunxiang Zhang

Authors
  1. Junting Luo
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  2. Jingqi Zhao
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  3. Zheyi Yang
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  4. Yongbo Jin
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  5. Chunxiang Zhang
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Contributions

Junting Luo contributed to the conception of the study. Jingqi Zhao performed the data analyses and wrote the manuscript. Zheyi Yang made an important contribution to the simulation and data analysis process. Yongbo Jin performed the experiment. Chunxiang Zhang helped perform the analysis with constructive discussions.

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Correspondence to Junting Luo.

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Luo, J., Zhao, J., Yang, Z. et al. Microstructure prediction of multi-directional forging for 30Cr2Ni4MoV steel by the secondary development of Deform software and BP neural network. Int J Adv Manuf Technol 119, 2971–2984 (2022). https://doi.org/10.1007/s00170-021-08615-w

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  • DOI: https://doi.org/10.1007/s00170-021-08615-w

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