Skip to main content
SpringerLink
Log in

A double-layer optimization method for forging process parameters of hinge beam structure and size of intermediate billet

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

A double-layer optimization method for the forging process parameters of a hinge beam structure and the design of intermediate billet structure is proposed. First, a primary optimization system for the forging process parameters of hinge beam was established based on orthogonal experiments and range analysis method, and the optimal combination of forging process parameters was selected. On this basis, a mathematical model of the secondary optimization of the final forging of the hinge beam was established. Through radial basis function (RBF) agent model and multi-island genetic algorithm, the secondary optimization of intermediate billet structure of hinge beam was carried out, which improved the grain refinement degree of final forging and reduced the forming load required for forging. Through numerical simulation and optimization design of forging process, the quality and performance of final forging of hinge beam were improved, which has certain guiding significance for the actual production of hinge beam.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Y. Yang, J. Wen, Y. Li and X. Fang, The new understanding of increasing the quality of diamond depend on large-size of cubic hinge apparatus, Superhard Material Engineering, 25 (6) (2022) 15–19.

    Google Scholar 

  2. X. Fang, J. Wen and Y. Yang, Rapid development of largescale cubic hinge press and relevant issues in China, Super-hard Material Engineering, 23 (1) (2011) 42–45.

    Google Scholar 

  3. J. Zhang, F. Liu, J. Wu, Y. Liu, Q. Hu, J. Liu, A. Liang, Q. Wang and D. He, Experimental study on the pressure-generation efficiency and pressure-seal mechanism for large volume cubic press, The Review of Scientific Instruments, 89 (7) (2018) 075106.

    Article  Google Scholar 

  4. J. Liu, G. Shen, L. Sun, T. Huang and X. Jia, Forging process development and mass production of hinge beam forgings, Heavy Castings and Forgings (2) (2020) 11–13.

  5. J. Guo, X. Deng, B. Zheng and C. Wu, Design and optimization for hot forging process of Ti55531Titanium alloy twisting force arm, J. of Netshape Forming Engineering, 13 (2) (2021) 96–104.

    Google Scholar 

  6. Y. Yi, C. Liu and S. Huang, Simulation of dynamic recrystallization for 7050 aluminium alloy on platform of DEFORM-3D using cellular automaton, J. of Central South University (Science and Technology), 41 (5) (2010) 1814–1820.

    Google Scholar 

  7. H. C. Ji, Y. M. Li, W. D. Li, S. H. Xiao, J. S. Zhang and Y. H. Lu, Study on forging process of valve based on response surface method, Metalurgija, 59 (3) (2020) 321–324.

    Google Scholar 

  8. Y. Jia, H. Peng and H. Cao, Parameter optimization in hot precision forging process of synchronizer ring based on grey relational analysis and response surface method, Materials Research Express, 9 (4) (2022) 046517.

    Article  Google Scholar 

  9. J. O. Obiko, F. M. Mwema and H. Shangwira, Forging optimisation process using numerical simulation and Taguchi method, SN Applied Sciences, 2 (3) (2020) 1–9.

    Google Scholar 

  10. Y. Tao, J. Zhou, J. Cao, Y. Luo and B. Chen, Optimization design preform billet shape of 7050 aluminum alloy giant plane forgings based on electric field method and MBC toolbox, The International J. of Advanced Manufacturing Technology, 81 (1–4) (2015) 231–240.

    Article  Google Scholar 

  11. W. Gao and T. Ma, Analysis of forging method of large-scale hinge beam, Heavy Castings and Forgings (2) (2020) 14–15.

  12. A. Pandya Vishal and P. M. George, Analysis of die stress and forging force for DIN 1.2714 die material during closed die forging of anchor shackle, Materials Today, Proceedings, 45 (P6) (2021) 4695–4701.

    Article  Google Scholar 

  13. Y. Wu, R. C. Yan and E. W. Qin, Effect of grain boundary energy in recrystallization simulation by phase field method, Materials Science Forum, 993 (2020) 953–958.

    Article  Google Scholar 

  14. W. Y. Yang, H. M. Wang and L. F. Li, Dynamic recrystallization of ferrite in a low carbon steel with different minor microstructures, Acta Metallurgical Sinica, 37 (7) (2003) 609–619.

    Google Scholar 

  15. T. Al-Samman and G. G. Ottstein, Dynamic recrystallization during high temperature deformation of magnesium, Materials Science and Engineering A, 490 (1–2) (2008) 411–420.

    Article  Google Scholar 

  16. L. Chen, C. Li, M. Zheng, G. Chen and W. Chen, Numerical simulation of grain evolution in the forging process of steering arm, Die & Mould Industry, 44 (8) (2018) 15–19.

    Google Scholar 

  17. Z. J. Zhang, G. Z. Dai and S. N. Wu, Simulation of 42CrMo steel billet upsetting and its defects analyses during forming process based on the software DEFORM-3D, Materials Science & Engineering A, 499 (1–2) (2009) 49–52.

    Article  Google Scholar 

  18. Y. P. Yi, C. Liu and S. Q. Huang, Simulation of dynamic recrystallization for 7050 aluminium alloy on platform of DEFORM-3D using cellular automaton, J. of Central South University (Science and Technology), 41 (5) (2010) 1814–1820.

    Google Scholar 

  19. R. Li, B. Xu, Q. Zhang, X. Gu, G. Zheng, H. Ma and X. Jia, Finite-element analysis on pressure transfer mechanism in large-volume cubic press, High Pressure Research, 36 (4) (2016) 575–584.

    Article  Google Scholar 

  20. K. K. Prasad, S. K. Tamang and M. Chandrasekaran, Comparative study on cutting force simulation using DEFORM 3D software during high speed machining of Ti-6Al-4V, Key Engineering Materials, 6036 (2020) 50–56.

    Article  Google Scholar 

  21. J. Xie and J. Wang, Performance optimization of pinnate horizontal well in geothermal energy utilization with orthogonal test, Applied Thermal Engineering, 209 (2022) 118321.

    Article  Google Scholar 

  22. M. Cong, S. Zhang, D. Sun and K. Zhou, Optimization of preparation of foamed concrete based on orthogonal experiment and range analysis, Frontiers in Materials, 8 (2021) 778173.

    Article  Google Scholar 

  23. L. L. Dai, Study on optimization of operating parameters of contact mechanical seal based on orthogonal test, J. of Physics, Conference Series, 2137 (1) (2021) 012043.

    Article  Google Scholar 

  24. B. Xu, J. Liu and W. Lu, Optimization design of Y-shaped settling diversion wall based on orthogonal test, Machines, 10 (2) (2022) 91.

    Article  Google Scholar 

  25. W. Yi, P. Wang and T. Hongzhang, Optimization of tea garden trimmer blade based on FEM and orthogonal test, J. of Agricultural Mechanization Research, 42 (1) (2020) 8–13.

    Google Scholar 

  26. M. Meraz, J. Alvarez-Ramirez and E. Rodriguez, Multivariate rescaled range analysis, Physica A, Statistical Mechanics and its Applications, 589 (2022) 126631.

    Article  Google Scholar 

  27. A. Karolczuk and M. Kurek, Fatigue life uncertainty prediction using the monte carlo and latin hypercube sampling techniques under uniaxial and multiaxial cyclic loading, International J. of Fatigue, 160 (2022) 126631.

    Article  Google Scholar 

  28. X. Li, X. Han and Z. Chen, A multi-constraint failure-pursuing sampling method for reliability-based design optimization using adaptive Kriging, Engineering With Computers, 38 (2020) 297–310.

    Article  Google Scholar 

  29. Q. Wang, X. Su and Z. Yin, Optimization of automobile rear axle housing based on response surface approximation model, J. of Light Industry, 34 (1) (2019) 71–78.

    Google Scholar 

  30. Z. Yuan, L. Kong and D. Gao, Multi-objective approach to optimize cure process for thick composite based on multi-field coupled model with RBF surrogate model, Composites Communications, 24 (1) (2021) 100671.

    Article  Google Scholar 

  31. F. Xu, G. Sun and G. Li, Crashworthiness design of multi-component tailor-welded blank (TWB) structures, Structural and Multidisciplinary Optimization, 48 (3) (2013) 653–667.

    Article  Google Scholar 

  32. W. Zhou, X. Yan, C. Chen and M. Guo, Optimization of RBF neural networks using a rough K-means algorithm and application to naphtha dry point soft sensors, J. of Chemical Engineering of Japan, 46 (7) (2013) 501–508.

    Article  Google Scholar 

  33. G. Xie, T. Wang and L. Wang, Cemented carbide layer thickness optimization of carbide anvil based on thermodynamic coupling, Meckanica, 28 (5) (2022) 401–409.

    Google Scholar 

  34. S. Z. Feng, Y. Xu, X. Han, Z. X. Li and I. Atilla, A phase field and deep-learning based approach for accurate prediction of structural residual useful life, Computer Methods in Applied Mechanics and Engineering, 383 (2021) 113885.

    Article  MathSciNet  Google Scholar 

  35. Y. Niu, X. Xu and S. Guo, Structural optimization design of typical adhesive bonded sandwich T-joints based on progressive damage analysis and multi-island genetic algorithm, J. of Sandwich Structures and Materials, 23 (8) (2021) 3932–3965.

    Article  Google Scholar 

  36. S. Z. Feng, X. Han, Z. J. Ma, K. Grzegorz and Z. X. Li, Data-driven algorithm for real-time fatigue life prediction of structures with stochastic parameters, Computer Methods in Applied Mechanics and Engineering, 372 (2020) 113373.

    Article  MathSciNet  Google Scholar 

  37. X. Hu, X. Chen and Y. Zhao, Optimization design of satellite separation systems based on multi-island genetic algorithm, Advances in Space Research, 53 (5) (2013) 870–876.

    Article  Google Scholar 

  38. S. Chen and J. Wang, Coupled interpolating element-free Galerkin scaled boundary method and finite element method for crack problems, Scientia Sinica, 48 (2018) 024601.

    Google Scholar 

  39. Y. Chu, Y. Xu and Y. Zou, Dynamic parameter optimization of one ramming mechanism based on multi-island genetic algorithm, J. of Physics, Conference Series, 1345 (4) (2019) 042065.

    Article  Google Scholar 

  40. S. Chen, W. Wang and X. Zhao, An interpolating element-free Galerkin scaled boundary method applied to structural dynamic analysis, Applied Mathematical Modelling, 75 (2019) 494–505.

    Article  MathSciNet  MATH  Google Scholar 

  41. G. Xie, S. Zhang and L. Wang, Lightweight design of hinge beam based on Kriging agent model, J. of Mechanical Science and Technology, 36 (7) (2022) 3585–3595.

    Article  Google Scholar 

  42. G. Sun, L. Li, J. Fang and Q. Li, On lower confidence bound improvement matrix-based approaches for multiobjective Bayesian optimization and its applications to thin-walled structures, Thin-Walled Structures, 161 (2021) 107248.

    Article  Google Scholar 

  43. Y. C. Bai, H. S. Zhou and F. Lei, An improved numerically-stable equivalent static loads (ESLs) algorithm based on energy-scaling ratio for stiffness topology optimization under crash loads, Structural and Multidisciplinary Optimization, 59 (1) (2019) 117–130.

    Article  MathSciNet  Google Scholar 

  44. C. Lin, F. Gao and Y. Bai, Multiobjective reliability-based design optimisation for front structure of an electric vehicle using hybrid metamodel accuracy improvement strategy-based probabilistic sufficiency factor method, International J. of Crashworthiness, 23 (3–4) (2018) 290–301.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 52175256, and 52075500), Key Scientific and Technological Project of Henan Province (2321022 21040, and 23210222 1041), Key Scientific Project of Henan Province (No. 21111 0220200), and Major Science and Technology Projects of Longmen Laboratory (No. 231100220300).

Author information

Authors and Affiliations

  1. Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical, Henan International Joint Laboratory of Complex Mechanical Equipment Intelligent Monitoring and Control, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China

    Guizhong Xie, Shixin Zhang, Hao Li, Liangwen Wang, Wenliao Du, Chongmao Zhao & Hangqi Jia

Authors
  1. Guizhong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shixin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liangwen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenliao Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chongmao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hangqi Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shixin Zhang.

Additional information

Shixin Zhang graduated from Zhengzhou University of Light Industry, and has obtained a Master’s in a mechanical major from Zhengzhou University of Light Industry. His main research direction is mechanical structure optimization design.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, G., Zhang, S., Li, H. et al. A double-layer optimization method for forging process parameters of hinge beam structure and size of intermediate billet. J Mech Sci Technol 37, 5307–5319 (2023). https://doi.org/10.1007/s12206-023-0933-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-023-0933-4

Keywords

Search

Navigation