Skip to main content

Predictive Modeling of Metal Forming and Machining Processes Using Soft Computing

  • Chapter
  • 3018 Accesses

Part of the book series: Engineering Materials and Processes ((EMP))

Abstract

The finite element method has been a very effective tool in the modeling of metal forming and machining processes as it provides detailed information regarding the product during and after the processes. Analysis of the process often requires nonlinear elasto-plastic finite element formulation. The finite element method can also be used for finding out the stress distribution in the tool and stress/vibration analysis of the machines. Unlike the work material, the tools and machines undergo only elastic deformations. In spite of this, a non-linear analysis is often needed. The major drawback of the finite element method is that it requires a large computational time.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

9.9 References

  1. Fisher, R.A. (1926), The arrangement of field experiments, Journal of the Ministry of Agriculture of Great Britain, Vol. 33, pp. 503–513.

    Google Scholar 

  2. Plackett, R.L. and Burman, J.P. (1946), The design of optimum multifactorial experiments, Biometrica, Vol. 33, pp. 305–325.

    Article  MATH  Google Scholar 

  3. Logothetis, N. (1992), Managing for Total Quality: From Deming to Taguchi and SPC, Prentice-Hall of India Pvt. Ltd., New Delhi.

    Google Scholar 

  4. Tanaka, H. (1987), Fuzzy data analysis by possibilistic linear models, Fuzzy Sets Sys, Vol. 24, pp. 363–375.

    Article  MATH  Google Scholar 

  5. Lawrence, M. and Petterson, A. (1998), BrainMaker User’s Guide and Reference Manual, 7-th Edition. California Scientific Software, Nevada City, California

    Google Scholar 

  6. Dixit, U.S. and Chandra, S. (2003), A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold rolling process, Int. J. Adv. Manuf. Technol., Vol. 22, pp. 883–889.

    Article  Google Scholar 

  7. Kohli, A. and Dixit, U.S. (2005), A neural-network-based methodology for the prediction of surface roughness in a turning process, Int. J. Adv. Mnuf. Technol., Vol. 25, pp. 118–129.

    Article  Google Scholar 

  8. Haykin S., (1996), Adaptive Filter Theory, 3-rd edn., Prentice-Hall, New York.

    Google Scholar 

  9. Ishibuchi, H. and Tanaka, H. (1991), Regression analysis with interval model by neural networks. Proceedings of the IEEE Int. Joint Conf. on Neural Networks, Singapore, pp. 1594–1599.

    Google Scholar 

  10. Sonar, D.K., Dixit, U.S. and Ojha, D.K. (2006), The application of a radial basis function neural network for predicting the surface roughness in a turning process, Int. J. Adv. Mnuf. Technol., Vol. 27, pp. 661–666.

    Article  Google Scholar 

  11. Chen, J.C. and Black J.T. (1997), A fuzzy-nets in-process (FNIP) system for toolbreakage monitoring in end-milling operations, Int. J. Mach. Tools and Manuf., Vol. 37, pp. 783–800.

    Article  Google Scholar 

  12. Chen, J.C. and Savage, M. (2001), A fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations, Int J Adv Manuf Technol Vol. 17, pp. 670–676.

    Article  Google Scholar 

  13. Abburi, N.R., and Dixit, U.S. (2006), A knowledge-based system for the prediction of surface roughness in turning process, Robotics and Computer-Integrated Manufacturing, Vol. 22,4, pp. 363–372.

    Article  Google Scholar 

  14. Jang, J.S.R. (1993), ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transaction on System, Man and Cybernetics, Vol. 23, pp. 665–685.

    Article  Google Scholar 

  15. Dong, W. and Shah, H.C. (1987), Vertex method for computing functions of fuzzy variables, Fuzzy Sets and Systems, Vol. 24, pp. 65–78.

    Article  MATH  MathSciNet  Google Scholar 

  16. Johnson W, Mellor, P.B. (1972), Engineering Plasticity, Von Nostrand Reinhold Company, London.

    Google Scholar 

  17. Dixit, U.S. (1997), Cold Flat Rolling: Modeling with Fuzzy Parameters, Anisotropic Effects and Residual Stresses, Ph.D. thesis, IIT Kanpur.

    Google Scholar 

  18. Shannon, C.E., (1948), The mathematical theory of communication, The Bell System Technical Journal, Vol. 27, 379–423, 623–656.

    MathSciNet  Google Scholar 

  19. De Luca, A., and Termini, A., (1972), A Definition of Nonprobablistic Entropy in the Setting of Fuzzy Set Theory, Information and Control, Vol. 20, pp. 301–312.

    Article  MathSciNet  Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag London Limited

About this chapter

Cite this chapter

(2008). Predictive Modeling of Metal Forming and Machining Processes Using Soft Computing. In: Modeling of Metal Forming and Machining Processes. Engineering Materials and Processes. Springer, London. https://doi.org/10.1007/978-1-84800-189-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-84800-189-3_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-188-6

  • Online ISBN: 978-1-84800-189-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics