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Investigation on forming–welding process chain for DC04 tube manufacturing using experiment and FEM simulation

  • Alexander Bauer
  • Yupiter H. P. ManurungEmail author
  • Joeran Sprungk
  • Marcel Graf
  • Birgit Awiszus
  • Keval Prajadhiana
ORIGINAL ARTICLE
  • 9 Downloads

Abstract

A chained forming–welding process is to be investigated and analyzed using experimental verification and numerical simulation in which the material and mechanical properties are fully transferred between processes. The investigated part is in the form of a tube with dimension of 300 mm (l) × 20 mm (OD) × 1.5 mm (t) made of a low carbon steel material DC04 commonly used for automotive parts and support structure. At first, a series of experiment using industrial U-/O-bending machine and fully automated robotic gas metal arc welding (GMAW) process on longitudinal slot were sequentially conducted and analyzed towards final geometrical change, macrostructure, and residual stress. Further, numerical simulation method using specialized FEM software Simufact.Forming and Simufact.Welding is developed to predict the major properties following the actual process parameters during experimental forming and welding process. Throughout the simulation of forming and welding process, additive isotropic hardening plasticity model based on von Mises yield criterion is selected with a wide range of operating temperature and Goldak’s double-ellipsoid is defined as welding heat source model. Based on the analysis outcome, it can be concluded that coupled forming–welding process simulation can be suitably implemented to forecast major material and mechanical properties of tube manufacturing within accepted range of error under consideration of material history.

Keywords

Forming simulation Welding simulation Coupled forming–welding Chained FEM simulation process U-/O-bending Robotic welding 

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Notes

Acknowledgements

The internationally collaborating authors would like to express their gratitude to Advanced Manufacturing Technology Excellence Centre (AMTEx) and Research Interest Group: Advanced Manufacturing Technology (RIG: AMT) at Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM) in Malaysia as well as Professorship Virtual Production Engineering at Chemnitz University of Technology (CUT) in Germany for encouraging this research and providing the equipment. The simulation and advanced equipment are implemented at CUT.

Funding information

This research is financially supported by international research grant of DAAD (Ref. Nr.: 57347629) and Geran Inisiatif Penyeliaan (GIP) from Phase 1/2016 with Project Code: 600-IRMI/GIP 5/3 (0019/2016).

References

  1. 1.
    Gebhart C (2014) Auf ewig zusammen—Numerische Simulation in der Verbindungstechnik—Schweißen. CADFEM J 2:24Google Scholar
  2. 2.
    Jiang P, Wang C, Zhou Q, Shao X, Shu L, Li X (2016) Optimization of laser welding process parameters of stainless steel 316L using FEM, Kriging and NSGA-II. Adv Eng Softw 99:146–160.  https://doi.org/10.1016/j.advengsoft.2016.06.006 CrossRefGoogle Scholar
  3. 3.
    Vetriselvan R, Devakumaran K, Sathiy P, Ravichandran G (2016) Transient out-of-plane distortion of multi-pass fillet welded tube to pipe T-joints. Def Technol 13(2):77–85.  https://doi.org/10.1016/j.dt.2016.06.002 CrossRefGoogle Scholar
  4. 4.
    Li Y, Zhao Y, Li Q, Wu A, Zhu R, Wang G (2017) Effects of welding condition on weld shape and distortion in electron beam welded Ti2AlNb alloy joints. Mater Des 114:226–233.  https://doi.org/10.1016/j.matdes.2016.11.083 CrossRefGoogle Scholar
  5. 5.
    Kouadri-Henni A, Seang C, Malard B, Klosek V (2017) Residual stress induced by laser welding process in the case of a dual-phase steel DP600: simulation and experimental approaches. Mater Des 123:89–102.  https://doi.org/10.2016/j.matdes.2017.03.022 CrossRefGoogle Scholar
  6. 6.
    Piekarska W, Goszczynska-Kroliszewska D, Domanski T, Bokota A (2017) Analytical and numerical model of laser welding phenomena with the initial preheating. Procedia Eng 177:149–154.  https://doi.org/10.1016/j.proeng.2017.02.206 CrossRefGoogle Scholar
  7. 7.
    Hassan HUI, Traphoner H, Guner A, Tekaya AE (2016) Accurate springback prediction in deep drawing using pre-stain based multiple cyclic stress-strain curve in finite element simulation. Int J Mech Sci 110:229–241.  https://doi.org/10.1016/j.ijmecsci.2016.03.014 CrossRefGoogle Scholar
  8. 8.
    Jamli MR, Arrifin AK, Wahab a DA (2014) Integration of feedforward neural network and finite element in draw-bend spring back prediction. Expert Syst Appl 41(8):3662–3670.  https://doi.org/10.1016/j.eswa.2013.12.006 CrossRefGoogle Scholar
  9. 9.
    Loose T, Klöppel T (2015) An LS-DYNA material model for the consistent simulation of welding forming and heat treatment. Präsentation: 11th International Seminar: “Numerical Analysis of Weldability”, SeggauGoogle Scholar
  10. 10.
    Loose T (2015) Einbindung der Schweißsimulation in die Fertigungssimulation mit SimWeld and DynaWeld: Umformen Schweißen Wärmebehandeln. Präsentation: DVS Congress – Workshop: Anwendungsnahe Schweißsimulation, NürnbergGoogle Scholar
  11. 11.
    Schafstall H, Neubauer I, Litzkow J (2015) Einführung in die Prozesskettensimulation mit Simufact am Beispiel einer Crashbox—Von der Umformsimulation über das thermische Fügen zum Crash. Präsentation: 16. Simufact RoundTable MarburgGoogle Scholar
  12. 12.
    Adams T.-E, Härtel S, Hälsig A, Mayr P, Awiszus B (2017) Property improvement of welding seams due to an inline hot forming process. YPIC, 3rd Young Welding Professionals International Conference Halle-SaaleGoogle Scholar
  13. 13.
    Oeckerath A, Wolf K (2010) Improved product design using mapping in manufacturing process chains. L-S DYNA Forum, BambergGoogle Scholar
  14. 14.
    Govik A, Nilsson L, Moshfegh R (2012) Finite element simulation of the manufacturing process chain of a sheet metal assembly. J Mater Process Technol 212(7):1453–1462.  https://doi.org/10.1016/j.jmatprotec.2012.02.012 CrossRefGoogle Scholar
  15. 15.
    Zaeh MF, Roeren S (2005) One modified FE-model to simulate the process chain of forming and welding. Join TechnolGoogle Scholar
  16. 16.
    Zaeh MF, Tekkaya AE, Bierman B, Zabel A, Lamghorst M, Schober A (2009) Integrated simulation of the process chain composite extrusion-milling-welding for lightweight frame structure. Prod Eng 3(4):441–451.  https://doi.org/10.1007/s11740-009-0190-0 CrossRefGoogle Scholar
  17. 17.
    Afazof SM (2013) Modelling and simulation of manufacturing process chains. CIRP J Manuf Sci Technol 6(1):70–77.  https://doi.org/10.1016/j.cirpj.2012.10.005 CrossRefGoogle Scholar
  18. 18.
    Milenin A, Pernach M, Rauch R, Kuziak R, Zygmunt T, Pietrzyk M (2017) Modelling and optimization of the manufacturing chain for rails. Procedia Eng 207:2011–2016.  https://doi.org/10.1016/j.proeng.2017.10.1112 Google Scholar
  19. 19.
    Christian D, Konrad W, Wofgang T (2016) Continuous generating grinding: machine tool optimization by coupled manufacturing simulation. J Manuf Process 23:211–221.  https://doi.org/10.1016/j.jmapro.2016.06.024 CrossRefGoogle Scholar
  20. 20.
    Soekjeon H, Lindgren LE (2004) Simulating a chain manufacturing process using a geometry-based finite element code with adaptive meshing. Finite Elem Anal Des 40(5–6):511–528.  https://doi.org/10.1016/S0168-874(X)00075-1 Google Scholar
  21. 21.
    Ahmadi H, Zohoor M (2017) Investigation of the effective parameters in tube hydroforming process by using experimental and finite element method for manufacturing of tee joint products. Int J Adv Manuf Technol 93(1):393–405.  https://doi.org/10.1007/s00170-016-9690-1 CrossRefGoogle Scholar
  22. 22.
    Ren N, Yang H, Zhan M, Zhang ZY, Jiang HM, Diao KS, Chen XP (2013) Effect of weld characteristic on the formability of welded tubes in NC bending process. Int J Adv Manuf Technol 69:181–195.  https://doi.org/10.10007/s00170-013-5015-9 CrossRefGoogle Scholar
  23. 23.
    Han C, Feng H, Yuan SJ (2016) Springback and compensation of bending for hydroforming of advanced high-strength steel welded tubes. Int J Adv Manuf Technol 89:3619–3629.  https://doi.org/10.1007/s00170-016-9319-4 CrossRefGoogle Scholar
  24. 24.
    Zhu S, Liu J, Yin FL, Meng FJ, Chang TQ (2015) An innovative forming method based on an arc welding robot. Int J Adv Manuf Technol 84:1531–1538.  https://doi.org/10.1007/s00170-0157582-4 Google Scholar
  25. 25.
    Zhou Y, Li P, Li M, Wang L, Sun S (2017) Residual stress and springback analysis for 304 stainless steel tubes in flexible-bending process. Int J Adv Manuf Technol 94:1317–1325.  https://doi.org/10.1007/s00170-014-6675-9 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Professorship of Virtual Production EngineeringChemnitz University of TechnologyChemnitzGermany
  2. 2.Faculty of Mechanical EngineeringUiTM Shah AlamShah AlamMalaysia
  3. 3.University of Applied SciencesBochumGermany

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