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Experimental and numerical prediction on square cup punch–die misalignment during the deep drawing process

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Abstract

Punch–die misalignment is one of the factors that can contribute to drawn cup defects such as thinning and tearing. Deep drawing is a closed-die process, and defects can be identified only after the drawing is finished. In this work, the effect of punch–die misalignment severity on the drawing force and wall thickness distribution of electrolytic zinc-coated steel blank (SECC) was investigated. The stress–strain diagram and forming limit diagram of SECC material was determined and simulated using the Hill’48 model. Two conditions of punch–die misalignment were studied: single-axis and multi-axis misalignment. A high punch–die misalignment severity contributes to the increment in the drawing force. Furthermore, wall thickness distribution becomes non-uniform, and the thinning pattern increases due to the greater misalignment severity. For validation, an experiment was conducted on a universal tensile machine.

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References

  1. Atul ST, Babu MCL (2018) A review on effect of thinning, wrinkling and spring-back on deep drawing process. Proc Inst Mech Eng B J Eng Manuf 233:1–26

    Google Scholar 

  2. Berger M, Zussman E (2002) On-line thinning measurement in the deep drawing process. J Manuf Sci Eng 124(2):420–425

    Article  Google Scholar 

  3. Colgan M, Monaghan J (2003) Deep drawing process: analysis and experiment. J Mater Process Technol 132(1):35–41

    Article  Google Scholar 

  4. Hill R (1948) A theory of the yielding and plastic flow of anisotropic metals. Proc Roy Soc A Math, Phys Eng Sci 193(1033):281–297

    MathSciNet  MATH  Google Scholar 

  5. Huang Y, Chen J (1996) Influence of the tool clearance in the cylindrical cup-drawing process. J Mater Process Technol 0136(95):4–13

    Article  Google Scholar 

  6. Intarakumthornchai T, Jirathearant S, Thongprasert S, Dechaumphai P (2010) FEA-based optimization of blank holder force and pressure for hydromechanical deep drawing of parabolic cup using greedy search and RSM methods. Eng J 14(2):15–32

    Article  Google Scholar 

  7. Joshi AR, Kothari KD, Jhala RL (2013) Effects of different parameters on deep drawing process: review. Int J Eng Res Technoly (IJERT) 2(3):1–5

    Google Scholar 

  8. Karupannasamy DK et al (2012) Modelling mixed lubrication for deep drawing processes. Wear. Elsevier 294–295:296–304

    Article  Google Scholar 

  9. Kibe Y, Okada Y, Mitsui K (2007) Machining accuracy for shearing process of thin-sheet metals—development of initial tool position adjustment system. Int J Mach Tool Manu 47:1728–1737

    Article  Google Scholar 

  10. Kitayama S, Hamano S, Yamazaki K, Kubo T, Nishikawa H, Kinoshita H (2010) A closed-loop type algorithm for determination of variable blank holder force trajectory and its application to square cup deep drawing. Int J Adv Manuf Technol 51:507–517

    Article  Google Scholar 

  11. Nagda PS, Bhatt PS, Shah MK (2017) Finite element simulation of deep drawing process to minimize earing. World Acade Sci Eng Technol Int J Mechan Mechatronics Eng 11(2):413–416

    Google Scholar 

  12. Ogawa T et al (2016) Analysis of square cup deep-drawing test of pure titanium. J Phys Conf Ser 734(3):032072

    Article  Google Scholar 

  13. Padmanabhan R, Oliveira MC, Alves JL, Menezes LF (2007) Influence of process parameters on the deep drawing of stainless steel. Finite Elem Anal Des 43(14):1062–1067

    Article  Google Scholar 

  14. Patekar S (2015) Optimization of thinning in deep drawing process using grey wolf optimizer algorithm. Int Eng Res J (IERJ) 2:5722–5726

    Google Scholar 

  15. Ramezani M, Ripin ZM (2012) Analysis of deep drawing of sheet metal using the Marform process. Int J Adv Manuf Technol 59(5–8):491–505

    Article  Google Scholar 

  16. Signorelli JW, Serenelli MJ, Bertinetti MA (2012) Experimental and numerical study of the role of crystallographic texture on the formability of an electro-galvanized steel sheet. J Mater Process Tech. Elsevier B.V. 212(6):1367–1376

    Article  Google Scholar 

  17. Slavič J, Bolka Š, Bratuš V, Boltežar M (2014) A novel laboratory blanking apparatus for the experimental identification of blanking parameters. J Mater Process Tech 214:507–513

    Article  Google Scholar 

  18. Szumera J (2003) The metal stamping process: your product from concept to customer, 1st ed. Industrial Press Inc., New York

    Google Scholar 

  19. Tetzel H, Rathmann L, Heinrich L (2016) Simulation accurancy of a multistage micro deep drawing process. J Technol Plasticity 41(1):1–11

    Google Scholar 

  20. Tian S, Feng Y, Gao Y (2015) Design and application of a novel cone-shaped blank holder for precision stamping process. Proc CIRP. Elsevier B.V. 27:309–312

    Article  Google Scholar 

  21. Vairavan H, Abdullah AB (2017) Die-punch alignment and its effect on the thinning pattern in the square-shaped deep drawing of aluminium alloy. Int J Mater Prod Technol 54:147–164

    Article  Google Scholar 

  22. Yang C, Li P, Fan L (2014) Blank shape design for sheet metal forming based on geometrical resemblance. Proc Eng 81:1487–1492

    Article  Google Scholar 

  23. Zein H, el Sherbiny M, Abd-Rabou M, el shazly M (2014) Thinning and spring back prediction of sheet metal in the deep drawing process. J Mater Des 53:797–808

    Article  Google Scholar 

  24. Zoesch A, Wiener T, Kuhl M (2015) Zero defect manufacturing: detection of cracks and thinning of material during deep drawing processes. Proc CIRP. Elsevier 33:179–184

    Article  Google Scholar 

  25. Zorn W et al (2019) Potential of the force distribution measurement in deep drawing processes for increasing the process quality. Int J Mech Eng Robot Res 8(3):449–453

    Article  Google Scholar 

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Acknowledgements

The authors express their gratitude to the School of Mechanical Engineering, Universiti Sains Malaysia, Penang and Public Service Department of Malaysia for the scholarship support under the HLP programme.

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A.A.G. is the main author and contributed in drafting the article, A.B.A. contributed in editing the article and acted as supervisor and A.A.G. and J.I.M. contributed in simulation works.

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Correspondence to Ahmad Baharuddin Abdullah.

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Abdul Ghafar, A., Abdullah, A.B. & Mahmood, J.I. Experimental and numerical prediction on square cup punch–die misalignment during the deep drawing process. Int J Adv Manuf Technol 113, 379–388 (2021). https://doi.org/10.1007/s00170-021-06595-5

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