Skip to main content
SpringerLink
Log in

An intelligent process model: predicting springback in single point incremental forming

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

Abstract

This paper proposes an intelligent process model (IPM), founded on the concept of data mining, for predicting springback in the context of sheet metal forming, in particular, single point incremental forming (SPIF). A limitation with the SPIF process is that the application of the process results in geometric deviations, which means that the resulting shape is not necessarily the desired shape. Errors are introduced in a nonlinear manner for a variety of reasons, but a contributor is the geometry of the desired shape. A local geometry matrix (LGM) representation is used that allows the capture of local geometries in such a way that they are suited to input to a classifier generator. It is demonstrated that a rule-based classifier can be used to train the classifier and generate a classification model. The resulting model can then be used to predict errors with respect to new shapes so that some correction strategy can be applied. The reported evaluation of the proposed IPM indicates that very promising results can be obtained with regard to reducing the shape deviations due to springback.

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. Allwood JM, Utsunomiya H (2006) A survey of flexible forming processes in Japan. Int J Mach Tools Manuf 46(15):1939–1960

    Article  Google Scholar 

  2. Ambrogio G, Costantino I, De Napoli L, Filice L, Fratini L, Muzzupappa M (2004) Influence of some relevant process parameters on the dimensional accuracy in incremental forming: a numerical and experimental investigation. J Mater Process Technol 153-154:501–507

    Article  Google Scholar 

  3. Ambrogio G, De Napoli L, Filice L (2009) A novel approach based on multiple back-drawing incremental forming to reduce geometry deviation. Int J Mater Form 2(1):9–12

    Article  Google Scholar 

  4. Bambach M (2010) A geometric model of the kinematics of incremental sheet forming for the prediction of membrane strains and sheet thickness. J Mater Process Technol 210(12):1562–1573

    Article  Google Scholar 

  5. Bambach M, Araghi B T, Hirt G (2009) Strategies to improve the geometric accuracy in asymmetric single point incremental forming. Prod Eng Res Devel 3(2):145–156

    Article  Google Scholar 

  6. Behera AK, Verbert J, Lauwers B, Duflou JR (2013) Tool path compensation strategies for single point incremental sheet forming using multivariate adaptive regression splines. Comput Aided Des 45(3):575–590

    Article  Google Scholar 

  7. Dearden G, Edwardson SP, Abed E, Bartkowiak K, Watkins KG (2006) Correction of distortion and design shape in aluminium structures using laser forming. In: 25th International Congress on Applications of Lasers and Electro Optics(ICALEO), pp 813–817

  8. Dirikolu MH, Akdemir E (2004) Computer aided modelling of flexible forming processes. J Mater Process Technol 148(3):376–381

    Article  Google Scholar 

  9. Dunston S, Ranjithan S, Bernold E (1996) Neural network model for the automated control of springback in rebars. IEEE Expert: Intell Syst Appl 11(4):45–49

    Article  Google Scholar 

  10. Edwardson SP, Watkins KG, Dearden G, Magee J (2001) Generation of 3D shapes using a laser forming technique. In: Proceedings of International Congress on Applications of Lasers and Electro Optics ICALEO, pp 2–5

  11. Egerton PA, Hall W (1998) Computer graphics. Mathematical first steps. Simon and Schuster International

  12. El-Salhi S, Coenen F, Dixon C, Khan MS (2012) Identification of correlations between 3D surfaces using data mining techniques: predicting springback in sheet metal forming. In: SGAI-International Conference on Artificial Intelligence, pp 391–404

  13. El-Salhi S, Coenen F, Dixon C, Khan MS (2013) Predicting Features in Complex 3D Surfaces Using a Point Series Representation: A Case Study in Sheet Metal Forming. In: Advanced Data Mining and Applications, Springer, LNCS, vol 8346, pp 505–516

  14. Hadoush A, van den Boogaard AH (2012) Efficient implicit simulation of incremental sheet forming. Int J Numer Methods Eng 90(5):597–612

    Article  MATH  Google Scholar 

  15. Hirt G, Kopp R, Ames J, Bambach M (2004) Forming strategies and process modelling for CNC incremental sheet forming. CIRP Annals- Manuf Technol 53(1):203–206

    Article  Google Scholar 

  16. Inamdar M, Date PP, Narasimhan K, Maiti SK, Singh UP (2000) Development of an artificial neural network to predict springback in Air Vee bending. Int J Adv Manuf Technol 16(5):376–381

    Article  Google Scholar 

  17. Jeswiet J, Geiger M, Engel U, Kleiner M, Schikorra M, Duflou J, Neugebauer R, Bariani P, Bruschi S (2008) Metal forming since 2000. CIRP J Manuf Sci Technol 1:2–17

    Article  Google Scholar 

  18. Junk S, Hirt G, Chouvalova I (2003) Forming strategies and tools in incremental sheet forming. In: Proceedings of the 10th International Conference on Sheet Metal (SHEMET), pp 57–64

  19. Khan M S, Coenen F, Dixon C, El-Salhi S (2012) Classification based 3-D surface analysis: predicting springback in sheet metal forming. J Theor Appl Comput Sci 6(2):45–59

    Google Scholar 

  20. Khan M S, Coenen F, Dixon C, El-Salhi S (2012) Finding correlations between 3-D surfaces: a study in asymmetric incremental sheet forming. In: Proc. Machine Learning and Data Mining in Pattern Recognition (MLDM’12), Springer LNAI 7376, pp 336–379

  21. Kim DJ, Kim BM (2000) Application of neural network and FEM for metal forming processes. Int. J. Mach. Tools Manuf. 40(6):911–925

    Article  Google Scholar 

  22. Kinsey B, Cao J, Solla S (2000) Consistent and minimal springback using a stepped binder force trajectory and neural network control. J Eng Mater Technol 122(1113):113–118

    Google Scholar 

  23. Li RJ, Li MZ, Qiu NJ, Cai ZY (2014) Surface flexible rolling for three-dimensional sheet metal parts. J Mater Process Technol 214(2):380–389

    Article  Google Scholar 

  24. Lu B, Chen J, Ou H, Cao J (2013) Feature-based tool path generation approach for incremental sheet forming process. J Mater Process Technol 213:1221–1223

    Article  Google Scholar 

  25. Manabe K, Yang M, Yoshihara S (1998) Artificial intelligence identification of process parameters and adaptive control system for deep drawing process. J Mater Process Technol 80-81:421–426

    Article  Google Scholar 

  26. Micari F, Ambrogio G, Filice L (2007) Shape and dimensional accuracy in single point incremental forming: State of the art and future trends. J Mater Process Technol 191(1-3):390–395

    Article  Google Scholar 

  27. Pathak KK, Panthi S, Ramakrishnan N (2005) Application of neural network in sheet metal bending process. Def Sci J 55(2):125–131

    Article  Google Scholar 

  28. Rauch M, Hascoet J, Hamman J, Plenel Y (2009) Tool path programming optimization for incremental sheet forming application. Comput Aided Des 41:877–85

    Article  Google Scholar 

  29. Ruffini R, Cao J (1998) Using neural network for springback minimization in a channel forming process. J Mater Manuf 107(5):65–73

    Google Scholar 

  30. Skjoedt M, Hancock M H, Bay N (2007) Creating helical tool paths for single point incremental forming. Key Eng Mater 344:583–590

    Article  Google Scholar 

  31. Thibaud S, Ben Hmida R, Richard F, Malécot P (2012) A fully parametric toolbox for the simulation of single point incremental sheet forming process: numerical feasibility and experimental validation. Simul Model Pract Theory 29:32–43

    Article  Google Scholar 

  32. Yin J, Li D (2004) Knowledge discovery from finite element simulation data. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, pp 1335–1340

  33. Zhang S, Luo C, Peng Y, Li D, Yang H (2003) Study on factors affecting springback and application of data mining in springback analysis. J Shanghai Jiaotong Univ E-8(2):192–196

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    Muhamad S. Khan, Frans Coenen, Clare Dixon & Subhieh El-Salhi

  2. Tecnalia, Parque Tecnológico, San Sebastián, Spain

    Mariluz Penalva & Asun Rivero

Authors
  1. Muhamad S. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frans Coenen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Clare Dixon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Subhieh El-Salhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mariluz Penalva
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Asun Rivero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Clare Dixon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, M.S., Coenen, F., Dixon, C. et al. An intelligent process model: predicting springback in single point incremental forming. Int J Adv Manuf Technol 76, 2071–2082 (2015). https://doi.org/10.1007/s00170-014-6431-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6431-1

Keywords

Search

Navigation