Optimal Parameters Estimation in AWJ Machining Process using Active Set Method

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Parameters estimation in abrasive waterjet (AWJ) machining process is a task of searching a set of values that lead to the optimal machining performance. This task is usually conducted by using soft computing techniques like genetic algorithms (GA) and simulated annealing (SA). However, we found that the objective function of the problem is a simple quadratic formula with box constraints. Accordingly many established optimization methods from quadratic programming study can be employed to search for the optimal parameter values. In this paper, we demonstrate the use of the active set method for solving the problem and show that this method can confidently outperform GA and SA both in the machining performance and computational times. These results suggest that this kind of problems should be addressed by using established gradient based optimization algorithms first before using soft computing techniques because the formers have better convergence property and also usually are faster than the latters.

Keywords

Abrasive waterjet machining Active set method, genetic algorithms Simulated annealing Quadratic programming 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

The author would like to thank the reviewers for useful comments. This research was supported by Ministry of Higher Education of Malaysia and Universiti Teknologi Malaysia under Exploratory Research Grant Scheme R.J130000.7828.4L095.

References

  1. 1.
    Hascalik, A., et al.: Effect of traverse speed on abrasive waterjet machining of Ti-6Al-4 V alloy. Mater. Des. 28, 1953–1957 (2007)Google Scholar
  2. 2.
    Akkurt, A., et al.: Effect of feed rate on surface roughness in abrasive waterjet cutting applications. J. Mater. Process. Technol. 147, 389–396 (2004)Google Scholar
  3. 3.
    About Waterjets: Basic Information, http://waterjets.org (2013)
  4. 4.
    Nalbant, M., et al.: Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Mater. Des. 28, 1379–1385 (2007)Google Scholar
  5. 5.
    Ozcelik, B., et al.: Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int. J. Adv. Manuf. Technol. 27, 234–241 (2005)Google Scholar
  6. 6.
    Caydas, U., Hascalik, A.: A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J. Mater. Process. Technol. 202, 574–582 (2008)Google Scholar
  7. 7.
    Chien, W.T., Chou, C.Y.: The predictive model for machinability of 304 stainless steel. J. Mater. Process. Technol. 118, 442–447 (2001)Google Scholar
  8. 8.
    Erzurumlu, T., Oktem, H.: Comparison of response surface model with neural network in determining the surface quality of moulded parts. Mater. Des. 28, 459–465(2007)Google Scholar
  9. 9.
    Kumar, S., Choudhury, S.K.: Prediction of wear and surface roughness in electro-discharge diamond grinding. J. Mater. Process. Technol. 191, 206–209 (2006)Google Scholar
  10. 10.
    Lee, B.Y., et al.: Modeling and optimization of drilling process. J. Mater. Process. Technol. 74, 149–157 (1998)Google Scholar
  11. 11.
    Nabil, B.F., Ridha, A.: Ground surface roughness prediction based upon experimental design and neural network models. Int. J. Adv. Manuf. Technol. 31, 24–36(2006)Google Scholar
  12. 12.
    Oktem, H., et al.: Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater. Des. 27, 735–744 (2006)Google Scholar
  13. 13.
    Risbood, K.A., et al.: Prediction of surface roughness and dimensional deviations by measuring cutting forces and vibrations in turning process. J. Mater. Process. Technol. 132, 203–214 (2003)Google Scholar
  14. 14.
    Spedding, T.A., Wang, Z.Q.: Study on modeling of wire EDM process. J. Mater. Process. Technol. 69, 18–28 (1997)Google Scholar
  15. 15.
    Zain, A.M., et al.: Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining. Eng. Comput. 27, 251–259 (2011)Google Scholar
  16. 16.
    Zain, A.M., et al.: Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst. Appl. 37, 4650–4659 (2010)Google Scholar
  17. 17.
    Zain, A.M., et al.: Simulated annealing to estimate the optimal cutting conditions for minimizing surface roughness in end milling Ti-6Al-4 V. Mach. Sci. Technol. 14, 43–62 (2010)Google Scholar
  18. 18.
    Ko, D.C., et al.: Application of neural network and Taguchi method to perform design in metal forming considering workability. Int. J. Mach. Tool Manuf. 39, 771–785 (1999)Google Scholar
  19. 19.
    Jegaraj, J.J.R., Babu, N.R.: A soft approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear. J. Mater. Process. Technol. 185, 217–227 (2007)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Faculty of Computing, N28-439-03Universiti Teknologi MalaysiaUTM Johor BahruMalaysia

Personalised recommendations