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Predicting stress distribution in cold-formed material with genetic programming

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Abstract

In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.

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References

  1. Lange K (1991) Handbook of metal forming. McGraw Hill, New York

  2. Wang JP, Tsai YH, Wang JJ (1997) The dynamic analysis of visioplasticity for the plane upsetting process by the flow-function elemental technique. J Mater Process Technol 63(1–3):738–743

  3. Wang JP, Lin YT, Tsai YH (1996) The finite flow-line regions approach to visioplasticity in plane-strain extrusion. J Mater Process Technol 58(2–3):308–313

  4. Mitchell TM (1997) Machine learning. McGraw Hill, New York

  5. Bäck T, Hammel U, Schwefel H-P (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1(1):3–17

    Article  Google Scholar 

  6. Koza JR (1994) Genetic Programming II. MIT, Cambridge

  7. Koza JR (1999) Genetic programming III. Morgan Kaufmann, San Francisco

  8. Brezocnik M, Balic J (2001) A genetic-based approach to simulation of self-organizing assembly. Robot Comput Integr Manuf 17(1–2):113–120

  9. Dobnik-Dubrovski P, Brezocnik M (2002) Using genetic programming to predict the macroporosity of woven cotton fabrics. Textile Res J 72(3):187–194

    Google Scholar 

  10. Wilson WRD (1997) Tribology in cold metal forming. J Manuf Sci Eng 119(4B):695–698

    Google Scholar 

  11. Koschmann T (1990) The common LISP companion. Wiley, New York

  12. Wolfram S (1996) The MATHEMATICA book. Cambridge University Press, Cambridge

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Correspondence to M. Brezocnik.

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Brezocnik, M., Gusel, L. Predicting stress distribution in cold-formed material with genetic programming. Int J Adv Manuf Technol 23, 467–474 (2004). https://doi.org/10.1007/s00170-003-1649-3

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  • DOI: https://doi.org/10.1007/s00170-003-1649-3

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