Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming

  • Subhieh El-Salhi
  • Frans Coenen
  • Clare Dixon
  • Muhammad Sulaiman Khan
Conference paper


A classification framework for identifying correlations between 3D surfaces in the context of sheet metal forming, especially Asymmetric Incremental Sheet Forming (AISF), is described. The objective is to predict “springback”, the deformation that results as a consequence of the application of a sheet metal forming processes. Central to the framework there are two proposed mechanisms to represent the geometry of 3D surfaces that are compatible with the concept of classification. The first is founded on the concept of a Local Geometry Matrix (LGM) that concisely describes the geometry surrounding a location in a 3D surface. The second, is founded on the concept of a Local Distance Measure (LDM) derived from the observation that springback is greater at locations that are away from edges and corners. The representations have been built into a classification framework directed at the prediction of springback values. The proposed framework and representations have been evaluated using two surfaces, a small and a large flat-topped pyramid, and by considering a variety of classification mechanisms and parameter settings.


Sheet Metal Local Binary Pattern Data Mining Technique Sheet Metal Form Single Point Incremental Form 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    G. Cafuta, N. Mole, and B. Łtok. An enhanced displacement adjustment method: Springback and thinning compensation. Materials and Design, 40:476 – 487, 2012.CrossRefGoogle Scholar
  2. 2.
    S. Chatti. Effect of the Elasticity Formulation in Finite Strain on Springback Prediction. Computers and Structures, 88(1112):796 – 805, 2010.CrossRefGoogle Scholar
  3. 3.
    S. Chatti and N. Hermi. The Effect of Non-linear Recovery on Springback Prediction. Computers and Structures, 89(13-14):1367 – 1377, 2011.CrossRefGoogle Scholar
  4. 4.
    W. Cohen. Fast effective rule induction. In Twelfth International Conference on Machine Learning, pages 115–123. Morgan Kaufmann, 1995.Google Scholar
  5. 5.
    M. Firat, B. Kaftanoglu, and O. Eser. Sheet Metal Forming Analyses With An Emphasis On the Springback Deformation. Journal of Materials Processing Technology, 196(1-3):135 – 148, 2008.CrossRefGoogle Scholar
  6. 6.
    Z. Fu, J. Mo, L. Chen, andW. Chen. Using Genetic Algorithm-Back Propagation Neural Network Prediction and Finite-Element Model Simulation to Optimize the Process of Multiple- Step Incremental Air-Bending Forming of Sheet Metal. Materials and Design, 31(1):267 – 277, 2010.Google Scholar
  7. 7.
    Z. Guo, L. Zhang, and D. Zhang. A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing, 19(6):1657–1663, 2010. 8. W. Hao and S. Duncan. Optimization of Tool Trajectory for Incremental Sheet FormingGoogle Scholar
  8. 8.
    Using Closed Loop Control. In Automation Science and Engineering (CASE), 2011 IEEE Conference on, pages 779 –784, 2011.Google Scholar
  9. 9.
    J. Jeswiet, F. Micari, G. Hirt, A. Bramley, J. Duflou, and J. Allwood. Asymmetric Single Point Incremental Forming of Sheet Metal. CIRP Annals - Manufacturing Technology, 54(2):88 – 114, 2005.CrossRefGoogle Scholar
  10. 10.
    G. John and P. Langley. Estimating Continuous Distributions in Bayesian Classifiers. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 338–345, San Mateo, 1995. Morgan Kaufmann.Google Scholar
  11. 11.
    R. Kazan, M. Firat, and A. Egrisogut Tiryaki. Prediction of Springback in Wipe-Bending Process of Sheet Metal Using Neural Network. Materials and Design, 30(2):418 – 423, 2009.CrossRefGoogle Scholar
  12. 12.
    W. Liu, Z. Liang, T. Huang, Y. Chen, and J. Lian. Process Optimal Ccontrol of Sheet Metal Forming Springback Based on Evolutionary Strategy. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, pages 7940 –7945, June 2008.Google Scholar
  13. 13.
    W. Liu, Q. Liu, F. Ruan, Z. Liang, and H. Qiu. Springback Prediction for Sheet Metal Forming Based on ga-ann Technology. Journal of Materials Processing Technology, 187-188:227 – 231, 2007.CrossRefGoogle Scholar
  14. 14.
    N. Narasimhan and M. Lovell. Predicting Springback in Sheet Metal Forming: An Explicit to Implicit Sequential Solution Procedure. Finite Elements in Analysis and Design, 33(1):29 – 42, 1999.MATHCrossRefGoogle Scholar
  15. 15.
    V. Nasrollahi and B. Arezoo. Prediction of Springback in Sheet Metal Components With Holes on the Bending Area, Using Experiments, Finite Element and Neural Networks. Materials and Design, 36:331 – 336, 2012.Google Scholar
  16. 16.
    R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA, 1993.Google Scholar
  17. 17.
    M. Tisza. Numerical Modelling and Simulation in Sheet Metal Forming. Journal of MaterialsProcessing Technology, 151(1-3):58 – 62, 2004.CrossRefGoogle Scholar
  18. 18.
    J. Yoon, F. Pourboghrat, K. Chung, and D. Yang. Springback Prediction For Sheet Metal Forming Process Using a 3d Hybrid Membrane/Shell Method. International Journal of Mechanical Sciences, 44(10):2133 – 2153, 2002.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Subhieh El-Salhi
    • 1
  • Frans Coenen
    • 1
  • Clare Dixon
    • 1
  • Muhammad Sulaiman Khan
    • 1
  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUnited Kingdom

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