Skip to main content

Performance Analysis of Turning Process via Particle Swarm Optimization

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 129))

Abstract

This paper describes the implementation of Particle Swarm Optimization (PSO) technique for CNC (computer numerically controlled) turning problem to find the optimal operating parameters such as cutting speed and feed rate such that the total production time is minimized subject to the constraints such as cutting force, power, tool-chip interface temperature and surface roughness of the product. An example is given to illustrate the working of Particle Swarm Optimization for optimizing the operating parameters. The results are compared with those obtained by Nelder Mead simplex method (NMS), boundary search method (BSP), genetic algorithm (GA) and simulated annealing (SA).

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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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.

References

  1. Gopalakrishnan B, Al-Khayyal Faiz (1991) Machine parameter selection for turning with constraints: an analytical approach based on geometric programming. International Journal of Production Research, pp. 1897–1908

    Google Scholar 

  2. Ermer D S (1971) Optimization of the constrained machining economics problem by geometric programming. Transactions ASME, Journal of Engineering for Industry, pp. 1067–1072

    Google Scholar 

  3. Draghici G, Paltinea C (1974) Calculation by convex mathematical programming. International Journal of Machine Tool Research, 14

    Google Scholar 

  4. Agapiou J S (1992) The optimization of machining operations based on a combined criterion, Part 1: The use of combined objectives in single pass operations. Transactions ASME, Journal of Engineering for Industry, 114, pp. 500–507

    Google Scholar 

  5. Agapiou J S (1992) The optimization of machining operations based on a combined criterion, Part 2: Multipass operations. Transactions ASME, Journal of Engineering for Industry, 114, pp. 508–13

    Google Scholar 

  6. Kennedy J, Eberhart R (1995) Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia , pp. 1942–1945

    Google Scholar 

  7. Saravanan R, Sekar G, Sachithanandam M (2000), Optimization of CNC machining operations subject to constraints using genetic algorithm (GA). Proceedings of the International Conference on Intelligent Flexible Autonomous Manufacturing Systems, Coimbatore, India, pp. 472–479

    Google Scholar 

  8. Saravanan R, Asokan P, Sachithanandam M (2001) Comparative Analysis of Conventional and Non-Conventional Optimisation Techniques for CNC Turning Process. International Journal of Advanced Manufacturing Technology, 17, pp. 471–476

    Article  Google Scholar 

  9. Mesquita Ruy, Krasteva E, Doytchinov S (1995) Computer-aided selection of optimum machining parameters in multi-pass turning. International Journal of Advanced Manufacturing Technology, 10, pp. 19–26

    Article  Google Scholar 

  10. Shi Y, Eberhart R C (May 1998) A Modified Particle Swarm Optimizer. In Proceedings of The IEEE Congress on Evolutionary Computation. Pages 69–73

    Google Scholar 

  11. Gilbert W W (1950) Economics of machining, Machining theory and practice. Aerican Society of metals. pp. 465–485

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Deep, K., Bansal, J.C. (2008). Performance Analysis of Turning Process via Particle Swarm Optimization. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78987-1_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78986-4

  • Online ISBN: 978-3-540-78987-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics