Skip to main content
Log in

Research on automatic analysis and layout system of the stamping process for automotive panels and its key technologies

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

With the shorter update cycle of automobiles and the development trend of fashion and streamlined appearance, the design of forming processes is becoming increasingly complex and demanding, and a large number of composite processes need to be used and arranged in 2–5 procedures to reduce production costs and shorten production cycles, which is usually done by trial and error. Aiming at the automatic recognition, analysis, and layout of process features, an intelligent system based on the computer-aided geometric design (CAGD) theory of complex curves and surfaces, process design knowledge, and multidisciplinary optimization techniques is proposed. The process feature analysis method based on discretization and merging idea has been proposed to split big and complex geometric form features and identify every process feature accurately and automatically, such as piercing, trimming, flanging, restriking, and its punching directions. The combinatorial optimization algorithm is proposed to merge the discrete features according to the best stamping direction, process rules, and the geometric parameters of the features, and a process constraint diagram with combined features is constructed, which is used as the initial solution and sequence constraint conditions for the process layout to effectively ensure the rationality of the process layout. Moreover, based on the die design rules, the simplified tools of the process features are created, and a rapid interference detection algorithm of the tools is proposed to realize the automatic layout and optimization of the stamping process to reduce the number of process steps as much as possible. Finally, the aforementioned algorithms are seamlessly integrated into the NX software to develop an automatic analysis and layout system. Case studies and industrial applications have shown the system’s strong capabilities in process analysis and layout of automotive panels, which can significantly improve design efficiency.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Tsai Y-L, You C-F, Lin J-Y, Liu K-Y (2010) Knowledge-based engineering for process planning and die design for automotive panels. Comput Aided Des Appl 7(1):75–87. https://doi.org/10.3722/cadaps.2010.75-87

    Article  Google Scholar 

  2. Qattawi A, Mayyas A, Dongri S, Omar M (2014) Knowledgebased systems in sheet metal stamping: a survey. Int J Comput Integr Manuf 27(8):707–718. https://doi.org/10.1080/0951192X.2013.834463

    Article  Google Scholar 

  3. Hu X, Ruan GQ (2021) Study on the mechanical behavior of quenching and partitioning sheet steel and its application to stamping of a hinge pillar inner panel. Strength Mater 53(4):627–635. https://doi.org/10.1007/s11223-021-00325-8

    Article  Google Scholar 

  4. Doshchechkina IV, D’yachenko SS, Ponomarenko IV, Tatarkina IS (2016) Improving the plasticity of thin cold-rolled steel sheet for cold stamping. Steel Transl 46(5):364–367. https://doi.org/10.3103/S0967091216050053

    Article  Google Scholar 

  5. Abbasi M, Bagheri B, Ketabchi M, Haghshenas DF (2012) Application of response surface methodology to drive GTN model parameters and determine the FLD of tailor welded blank. Comput Mater Sci 53(1):368–376. https://doi.org/10.1016/j.commatsci.2011.08.020

    Article  Google Scholar 

  6. Bagheri B, Abbasi M, Hamzeloo R (2021) Comparison of different welding methods on mechanical properties and formability behaviors of tailor welded blanks (TWB) made from AA6061 alloys. P I Mech Eng C-J Mec 235(12):2225–2237. https://doi.org/10.1177/0954406220952504

    Article  Google Scholar 

  7. Abbasi M, Bagheri B, Abdollahzadeh A, Moghaddam AO (2021) A different attempt to improve the formability of aluminum tailor welded blanks (TWB) produced by the FSW. Int J Mater Form 14:1189–1208. https://doi.org/10.1007/s12289-021-01632-w

    Article  Google Scholar 

  8. Abbasi M, Hamzeloo SR, Ketabchi M, Shafaat MA, Bagheri B (2014) Analytical method for prediction of weld line movement during stretch forming of tailor-welded blanks. Int J Adv Manuf Technol 73(5):999–1009. https://doi.org/10.1007/s00170-014-5850-3

    Article  Google Scholar 

  9. Liu HS, Cui JJ, Jiang KY, Zhou GT (2016) Cracking prediction in hot stamping of high-strength steel by a temperature-dependent forming limit surface approach. J of Materi Eng and Perform 25(11):4894–4901. https://doi.org/10.1007/s11665-016-2353-4

    Article  Google Scholar 

  10. Zhang W, Wang SQ, Chen JG, Cheng C, Zhang L (2018) Experimental and continuous stamping simulation study on surface wear of hardened steel mold. Int J Interact Des Manuf 12(4):1481–1494. https://doi.org/10.1007/s12008-018-0505-5

    Article  Google Scholar 

  11. Nnaji B-O, Kang T-S, Yeh S, Chen J-P (1991) Feature reasoning for sheet metal components. Int J Prod Res 29(9):1867–1896. https://doi.org/10.1080/00207549108948055

    Article  Google Scholar 

  12. Jagirdar R, Jain V-K, Batra J-L (2001) Characterization and identification of forming features for 3-D sheet metal components. Int J Mach Tool Manuf 41(9):1295–1322. https://doi.org/10.1016/S0890-6955(01)00006-2

    Article  Google Scholar 

  13. Eltahawy B, Ylihärsilä M, Virrankoski R, Petäjä E (2017) Towards a complete automation feature recognition system for sheet metal manufacturing. Int J Mech Aero Int Mech Manuf Eng 11:815–824. https://doi.org/10.5281/zenodo.1130217

  14. Salunkhe S, Teraiya S, Hussein H-M-A, Kumar S (2019) Smart system for feature recognition of sheet metal parts: a review. I-DAD 2018 2:535–549. https://doi.org/10.1007/978-981-13-2718-6_52

  15. Ghaffarishahri S, Rivest L (2020) Feature recognition for structural aerospace sheet metal parts. Comput Aided Des Appl 17(1):16–43. https://doi.org/10.14733/cadaps.2020.16-43

    Article  Google Scholar 

  16. Yang Y, Hinduja S, Owodunni OO, Heinemann R (2021) Recognition of features in sheet metal parts manufactured using progressive dies. Comput Aided Design 134:102991. https://doi.org/10.1016/j.cad.2021.102991

    Article  MathSciNet  Google Scholar 

  17. Chen L, Li Y, Zhao G, Zhang C, Gao F (2020) Multi-objective optimization and experimental investigation on hot extruded plate of high strength Al-Zn-Mg alloy. J Mater Res Technol 9(1):507–519. https://doi.org/10.1016/j.jmrt.2019.10.080

    Article  Google Scholar 

  18. Kayabasi O, Ekici B (2007) Automated design methodology for automobile side panel die using an effective optimization approach. Mater Des 28(10):2665–2672. https://doi.org/10.1016/j.matdes.2006.10.011

    Article  Google Scholar 

  19. Chen W, Yang J-C, Lin Z-Q (2002) Application of integrated formability analysis in designing die-face of automobile panel drawing dies. J Mater Process Technol 121(2-3):293–300. https://doi.org/10.1016/S0924-0136(01)01244-4

    Article  Google Scholar 

  20. Salunkhe S, Panghal D, Kumar S, Hussein H-M-A (2016) An expert system for process planning of sheet metal parts produced on compound die for use in stamping industries. Sādhanā 41:901–907. https://doi.org/10.1007/s12046-016-0521-8

    Article  Google Scholar 

  21. Wei D, Cui Z, Chen J (2008) Optimization and tolerance prediction of sheet metal forming process using response surface model. Comput Mater Sci 42(2):228–233. https://doi.org/10.1016/j.commatsci.2007.07.014

    Article  Google Scholar 

  22. Wei L, Yang Y (2008) Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm. J Mater Process Technol 208(1-3):499–506. https://doi.org/10.1016/j.jmatprotec.2008.01.014

    Article  Google Scholar 

  23. Li G, Yang P, Liang Z, Cui S (2019) Intelligent design and group assembly of male and female dies for hole piercing of automotive stamping dies. Int J Adv Manuf Technol 103(1):665–687. https://doi.org/10.1007/s00170-019-03576-7

    Article  Google Scholar 

  24. Makem J-E, Fogg H-J, Mukherjee N (2020) Automatic feature recognition using the Medial Axis for structured meshing of automotive body panels. Comput Aided Design 124:102845. https://doi.org/10.1016/j.cad.2020.102845

    Article  Google Scholar 

  25. Li J, Kong C, Zhou X-H (2020) Automatic design for trimming die insert of automotive panel. Int J Adv Manuf Technol 106(9):4451–4465. https://doi.org/10.1007/s00170-020-04938-2

    Article  Google Scholar 

  26. Lin B-T, Hsu S-H (2008) Automated design system for drawing dies. Expert Syst Appl 34(3):1586–1598. https://doi.org/10.1016/j.eswa.2007.01.024

    Article  MathSciNet  Google Scholar 

  27. Zhu JH, Gu XJ, Zhang WH, Beckers P (2013) Structural design of aircraft skin stretch-forming die using topology optimization. J Comput Appl Math 246:278–288. https://doi.org/10.1016/j.cam.2012.09.001

    Article  MathSciNet  MATH  Google Scholar 

  28. Xu D, Chen J, Tang Y, Cao J (2012) Topology optimization of die weight reduction for high-strength sheet metal stamping. Int J Mech Sci 59(1):73–82. https://doi.org/10.1016/j.ijmecsci.2012.03.006

    Article  Google Scholar 

  29. Wang H, Xie H, Liu Q, Shen Y, Wang P, Zhao L (2018) Structural topology optimization of a stamping die made from high-strength steel sheet metal based on load mapping. Struct Multidiscip O 58(2):769–784. https://doi.org/10.1007/s00158-018-1899-1

    Article  Google Scholar 

  30. Sheu JJ, Yang CH (2006) A simplified column model for the automatic design of the stamping die structure. J Mater Process Technol 177(1-3): 109-113. https://doi.org/10.1016/j.jmatprotec.2006.04.087

  31. Karen I, Kaya N, Öztürk F (2015) Intelligent die design optimization using enhanced differential evolution and response surface methodology. J Intell Manuf 26(5):1027–1038. https://doi.org/10.1007/s10845-013-0795-1

    Article  Google Scholar 

  32. Wang YL, Wang GY (2014) Study on modular design of trimming die structure for automotive panels. Adv Mat Res 945-949:73–76. https://doi.org/10.4028/www.scientific.net/amr.945-949.73

    Article  Google Scholar 

  33. Li G, Long X, Zhou M (2019) A new design method based on feature reusing of the non-standard cam structure for automotive panels stamping dies. J Intell Manuf 30(5):2085–2100. https://doi.org/10.1007/s10845-017-1368-5

    Article  Google Scholar 

  34. Deng W, Wang X, Zhao H, Yang X, He P (2008) Research on case representation and case retrieval algorithm for stamping die. In: 2008 Second International Symposium on Intelligent Information Technology Application, vol 1, pp 370–374. https://doi.org/10.1109/IITA.2008.181

    Chapter  Google Scholar 

  35. You C-F, Yang Y-H, Wang D-K (2011) Knowledge-based engineering supporting die face design of automotive panels. Industrial Design-New Frontiers:21–38. https://doi.org/10.5772/23305

  36. Piegl L-A, Tiller W (1999) Computing offsets of NURBS curves and surfaces. Comput Aided Design 31(2):147–156. https://doi.org/10.1016/S0010-4485(98)00066-9

    Article  MATH  Google Scholar 

Download references

Acknowledgements

We express our thanks to Renping Luo, Huping Sun, Yan Wang, and Jing Gao from General Motors’ stamping department for their great supports during the research.

Author information

Authors and Affiliations

Authors

Contributions

All authors have contributed to the conceptualization and design of this study. All authors have read and approved this manuscript.

Corresponding author

Correspondence to Xiong Hui Zhou.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

All authors have read and agreed to the published version of the manuscript.

Consent for publication

All authors have read and agreed to the published version of the manuscript.

Conflict of interest

The authors declare 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

Kong, C., Lei, J., Chao, J. et al. Research on automatic analysis and layout system of the stamping process for automotive panels and its key technologies. Int J Adv Manuf Technol 129, 4101–4120 (2023). https://doi.org/10.1007/s00170-023-12540-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-12540-5

Keywords

Navigation