Skip to main content
SpringerLink
Log in

Multi-Objective Evolutionary Neural Network Optimization of Process Parameters for Double-Stepped Tube Hydroforming

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

Achieving sharp corners without defects such as thinning and fractures is a primary objective in the manufacturing of double-stepped tubes. Therefore, selecting the optimal tube hydroforming (THF) process is crucial for improving the formability when manufacturing complex components. In this study, experimental and numerical study was conducted to analyze the radius of the corner and the thickness of the sample in the hydroforming process for double-stepped tubes. The pressure, axial feed, and friction coefficient were considered as input parameters, while the thinning ratio and corner filling were considered as output responses. The optimal hydroforming parameter combination for the corner filling ratio and the minimum thinning ratio was determined based on artificial neural networks. The values of the process parameters obtained from the finite element (FE) simulation and the artificial neural network (ANN) have a good correlation. The proposed method combining FE model and ANN is a precious tool for designing the THF process. Based on the results, it was confirmed that the feed rate has significant influence on the thinning ratio and corner filling.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Zhang, S. H. (1999). Developments in hydroforming. Journal of Materials Processing Technology, 91(1), 236–244. https://doi.org/10.1016/S0924-0136(98)00423-3

    Article  Google Scholar 

  2. Ghorbani-Menghari, H., Kim, J. H., Mohammadhosseinzadeh, M., Gorji, A., & Ghasempour-Mouziraji, M. (2020). Manufacturing of bent tubes with non-uniform curvature and cross-section using a novel hydroforming die: Experimental, finite element analysis and optimization. International Journal of Advanced Manufacturing Technology, 107, 1683–1695. https://doi.org/10.1007/s00170-020-05133-z

    Article  Google Scholar 

  3. Wang, X. S., Yuan, S., Song, P., & Xie, W. (2012). Plastic deformation on hydroforming of aluminum alloy tube with rectangular sections. Transactions of the Nonferrous Metals Society of China, 22, s350–s356. https://doi.org/10.1016/S1003-6326%2812%2961730-0

    Article  Google Scholar 

  4. Aue-U-Lan, Y., Ngaile, G., & Altan, T. (2004). Optimizing tube hydroforming using process simulation and experimental verification. Journal of Materials Processing Technology, 146, 137–143. https://doi.org/10.1016/S0924-0136(03)00854-9

    Article  Google Scholar 

  5. Mirzaali, M., Liaghat, G. H., Moslemi Naeini, H., Seyedkashi, S. M. H., & Shojaee, K. (2011). Optimization of tube hydroforming process using simulated annealing algorithm. Procedia Engineering, 10, 3012–3019. https://doi.org/10.1016/j.proeng.2011.04.499

    Article  Google Scholar 

  6. Li, B., Nye, T. J., & Metzger, R. D. (2005). Multi-objective optimization of forming parameters for tube hydroforming process based on the Taguchi method. International Journal of Advanced Manufacturing Technology, 28, 23–30. https://doi.org/10.1007/s00170-004-2338-6

    Article  Google Scholar 

  7. Abbassi, F., Belhadj, T., Mistou, S., & Zghal, A. (2013). Parameter identification of a mechanical ductile damage using artificial neural networks in sheet metal forming. Materials and Design, 45, 605–615. https://doi.org/10.1016/j.matdes.2012.09.032

    Article  Google Scholar 

  8. Zhou, C.-C., Yin, G.-F., & Hu, X.-B. (2009). Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Materials and Design, 30(4), 1209–1215. https://doi.org/10.1016/j.matdes.2008.06.006

    Article  Google Scholar 

  9. Elyasi, M., Bakhshi-Jooybari, M., Gorji, A., Hosseinipour, S. J., & Nourouzi, S. (2009). New die design for improvement of die corner filling in hydroforming of cylindrical stepped tubes. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(7), 821–827. https://doi.org/10.1243/09544054JEM1344

    Article  Google Scholar 

  10. Ghorbani Menghari, H., Ziaeipoor, H., Farzin, M., & Alves De Sousa, R. J. (2014). An approach to improve thickness distribution and corner filling of copper tubes during hydro-forming processes. Structural Engineering and Mechanics, 50(4), 563–573. https://doi.org/10.12989/sem.2014.50.4.563

    Article  Google Scholar 

  11. Ziaeipoor, H., Ghorbani Menghari, H., Alves De Sousa, R. J., Moosavi, H., Parastarfeizabadi, M., Farzin, M., & Sanaei, H. (2014). A novel approach in manufacturing two-stepped tubes using a multi-stage die in tube hydroforming process. International journal of precision engineering and manufacturing, 15(11), 2343–2350. https://doi.org/10.1007/s12541-014-0599-z

    Article  Google Scholar 

  12. Katayama, T., Nakamachi, E., Nakamura, Y., Ohata, T., Morishita, Y., & Murase, H. (2004). Development of process design system for press forming—multi-objective optimization of intermediate die shape in transfer forming. Journal of Materials Processing Technology, 155–156, 1564–1570. https://doi.org/10.1016/j.jmatprotec.2004.04.253

    Article  Google Scholar 

  13. Sobol, I. M. (1967). On the distribution of points in a cube and the approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics, 7(4), 86–112. https://doi.org/10.1016/0041-5553(67)90144-9

    Article  MathSciNet  MATH  Google Scholar 

  14. Ponce P. Inteligencia artificial con aplicaciones a la ingeniería. Alfaomega Grupo Editor, S.A. de C.V., México., ISBN: 978-607-7854-83-8.

  15. Marquardt, D. W. (1963). An Algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431. https://doi.org/10.1137/0111030

    Article  MathSciNet  MATH  Google Scholar 

  16. Hiroyasu, T., Miki, M., Kamiura, J., Watanabe, S., & Hiroyasu, H. (2002). Multi-objective optimization of diesel engine emissions and fuel economy using genetic algorithms and phenomenological model. SAE Technical Paper. https://doi.org/10.4271/2002-01-2778

    Article  MATH  Google Scholar 

  17. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  18. Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety, 91(9), 992–1007. https://doi.org/10.1016/j.ress.2005.11.018

    Article  Google Scholar 

  19. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2000). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. Parallel Problem Solving from Nature PPSN VI 2000. Springer. https://doi.org/10.1007/3-540-45356-3_83

  20. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman. Reading.

  21. Harish Ram, D. S., Bhuvaneswari, M. C., Prabhu, S. S. (2012). A Novel Framework for Applying Multiobjective GA and PSO Based Approaches for Simultaneous Area, Delay, and Power Optimization in High Level Synthesis of Datapaths. VLSI Des. https://doi.org/10.1155/2012/273276

  22. Garson, G. D. (1991). Interpreting neural network connection weights. AI Expert, 6(4), 46–51.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Korean government (MSIT) through the National Research Foundation of Korea (NRF) [grant number 2019R1A5A6099595].

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, 2 Busandaehak-Ro 63Beon-Gil, Busan, 46241, South Korea

    Hossein Ghorbani-Menghari, Parviz Kahhal, Majid Mohammadhosseinzadeh, Young Hoon Moon & Ji Hoon Kim

  2. Department of Mechanical Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran

    Parviz Kahhal

  3. Safety Performance CAE Team, Hyundai Motor Company, Hwaseong-si, Gyeonggi-do, 18270, South Korea

    Jaebong Jung

Authors
  1. Hossein Ghorbani-Menghari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parviz Kahhal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jaebong Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Majid Mohammadhosseinzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Young Hoon Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ji Hoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ji Hoon Kim.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghorbani-Menghari, H., Kahhal, P., Jung, J. et al. Multi-Objective Evolutionary Neural Network Optimization of Process Parameters for Double-Stepped Tube Hydroforming. Int. J. Precis. Eng. Manuf. 24, 915–929 (2023). https://doi.org/10.1007/s12541-023-00802-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-023-00802-x

Keywords

Search

Navigation