Skip to main content
Log in

Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Machining of particle-reinforced metal matrix composites has been considerably difficult due to the extremely abrasive nature of the reinforcements that causes rapid tool wear and high machining cost. Abrasive water jet (AWJ) machining has proven to be a viable technique to machine such materials compared to conventional machining processes. The present study is focused on the surface roughness of AWJ cut surfaces and genetic expression programming (GEP) was proposed to predict surface roughness in AWJ machining of 7075 Al alloy composites reinforced with Al2O3 particles. In the development predictive models, characteristics of materials such as size and weight fraction of reinforcement particles, and depth of cut were considered as model variables. The training and testing data sets were obtained from the well-established machining test results. The weight fraction of particle, size of particle, and depth of cut were used as independent input variables, while arithmetic mean of surface roughness, maximum roughness of profile height, and mean spacing of profile irregularity as dependent output variables. Different models for the output variables were predicted on the basis of training data set using GEP and accuracy of the best model was proved with testing data set. The test results showed that output variables increased with increasing input variables. The predicted results were compared with experimental results and found to be in good agreement with the experimentally observed ones.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Müller F, Monaghan J (2000) Non-conventional machining of particle reinforced metal matrix composites. Int J Mach Tool Manuf 40:1351–1366

    Article  Google Scholar 

  2. Manna A, Bhattacharayya B (2003) A study on machinability of Al/SiC-MMC. J Mater Process Technol 140:711–716

    Article  Google Scholar 

  3. Kannan S, Kishawy HA, Deiab IM, Surappa MK (2006) On the role of reinforcements on tool performance during cutting of metal matrix composites. J Manuf Process 8(2):67–75

    Article  Google Scholar 

  4. Brazil D, Monaghan J, Aspinwall DK, Ng EG (1997) Wear characterization of various diamond tooling when single point turning a particle reinforced metal matrix composite. Proc. of the IMC-14 Conf, Dublin, Ireland, pp. 143–152

  5. Weinert K (1993) A consideration of tool wear mechanism when machining metal matrix composites (MIMC). Ann CIRP 42(1):95–98

    Article  Google Scholar 

  6. Hung NP, Yang LJ, Leong KW (1994) Electro discharge machining of cast metal matrix composites. J Mater Process Technol 44:229–236

    Article  Google Scholar 

  7. Le Roux T, Wise MLH, Aspinwall DK (1993) The effect of electro discharge machining on the surface integrity of an aluminium-silicon carbide metal matrix composite. J Pro Adv Mater 3:233–241

    Google Scholar 

  8. Müller F, Monaghan J (1998) Electro discharge machining of a particle reinforced metal matrix composite. Proc of the 12th Int Sym for Electro Machining (ISEM XII), VDI-Berichte 1405, Aachen, pp. 513–522

  9. Lau WS, Lee WB, Pang SQ (1990) Pulsed ND: YAG laser cutting of carbon fiber composite materials. Ann CIRP 39(1):179–182

    Article  Google Scholar 

  10. Müller F, Monaghan J (1998) Laser cutting of particle reinforced metal matrix composites. Proc 6th SheMet Conf, Twente, Netherlands, pp. 93–101

  11. Hamatani G, Ramulu M (1990) Machinability of high temperature composite by abrasive waterjet. ASME J Eng Mat Tech 122:381–386

    Article  Google Scholar 

  12. Shanmugam DK, Chen FL, Siores E, Brandt M (2002) Comparative study of jetting machining technologies over laser machining technology for cutting composite materials. J Compos Struct 57:289–296

    Article  Google Scholar 

  13. Parikh PJ, Lam SS (2009) Parameter estimation for abrasive water jet machining process using neural networks. Int J Adv Manuf Technol 40:497–502

    Article  Google Scholar 

  14. Momber AW, Kovacevic R (1998) Principles of abrasive waterjet machining. Springer, London

    Google Scholar 

  15. Hashish M (1991) Optimization factors in abrasive waterjet machining. Transact ASME J Eng Ind 113:29–37

    Google Scholar 

  16. Jegaraj JJR, Babu NR (2005) A strategy for efficient and quality cutting of materials with abrasive waterjets considering the variation in orifice and focusing nozzle diameter. Int J Mach Tool Manuf 45:1443–1450

    Article  Google Scholar 

  17. Babu MK, Chetty OVK (2006) A study on the use of single mesh size abrasives in abrasive waterjet machining. Int J Adv Manuf Technol 29:532–540

    Article  Google Scholar 

  18. Akkurt A, Kulekci MK, Seker U, Ercan F (2004) Effect of feed rate on surface roughness in abrasive waterjet cutting applications. J Mater Process Technol 147:389–396

    Article  Google Scholar 

  19. Hascalik A, Çaydaş U, Gürün H (2007) Effect of traverse speed on abrasive waterjet machining of Ti-6Al-4V alloy. Mater Des 28:1953–1957

    Article  Google Scholar 

  20. Çaydaş U, Hasçalık A (2008) 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

    Article  Google Scholar 

  21. Hashish M (1984) A modeling study of metal cutting with abrasive waterjets. J Eng Mater Technol 106:88–100

    Article  Google Scholar 

  22. Arola D, Ramulu M (1993) Mechanism of material removal in abrasive waterjet machining of common aerospace materials. In: Proceedings of the seventh American waterjet conference, Seattle (WA), pp. 43–64

  23. Blickwedel H, Guo NS, Haferkamp H, Louis H (1991) Prediction of abrasive jet cutting performance and quality. Jet Cutting Technol 163–179

  24. Shanmugam DK, Masood SH (2009) An investigation on kerf characteristics in abrasive waterjet cutting of layered composites. J Mater Process Technol 209:3887–3893

    Article  Google Scholar 

  25. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT, Cambridge

    MATH  Google Scholar 

  26. Davidson JV, Savic DA, Walters GA (2003) Symbolic and numerical regression: experiments and application. Inf Sci 150(1/2):95–117

    Article  MathSciNet  Google Scholar 

  27. Ong CS, Huang JJ, Tzeng GH (2005) Expert Syst Appl 29(1):41–47

    Article  Google Scholar 

  28. Asbour AF, Alvarez LF, Toropov VV (2003) Comput Struct 81(5):331–338

    Article  Google Scholar 

  29. Ferreira C (2001) Compl Syst 13(2):87–129

    MATH  Google Scholar 

  30. Ferreira C (2001) Gene Expression Programming in Problem Solving. In: Invited Tutorial of the 6th Online World Conference on Soft Computing in Industrial Applications, pp. 10–24

  31. Ferreira C (2002) Gene expression programming: mathematical modelling by an artificial intelligence

  32. Kok M (2005) Production and mechanical properties of Al2O3 particle-reinforced 2024 aluminium alloy composites. J Mater Process Technol 161:381–387

    Article  Google Scholar 

  33. Sahin Y, Kok M, Celik H (2002) Tool wear and surface roughness of Al2O3 particle-reinforced aluminium alloy composites. J Mater Process Technol 128:280–291

    Article  Google Scholar 

  34. Eskil M, Kanca E (2008) A new formulation for martensite start temperature of Fe–Mn–Sishape memory alloys using genetic programming. Comput Mater Sci 43:774–784

    Article  Google Scholar 

  35. Baykasoglu A, Oztas A, Ozbay E (2009) Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches. Expert Syst Appl 36(3):6145–6155, April Part 2

    Article  Google Scholar 

  36. Eyercioglu O, Kanca E, Pala M, Ozbay E (2008) Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artifical neural networks. J Mater Process Technol 200:146–152

    Article  Google Scholar 

  37. Kanca E, Eskil M (2009) Comparison of new formulations for Martensite start temperature of Fe–Mn–Si shape memory alloys using geneting programming and neural networks. Comp, Mater Cont 270(1):1–31

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Metin Kök.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kök, M., Kanca, E. & Eyercioğlu, Ö. Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming. Int J Adv Manuf Technol 55, 955–968 (2011). https://doi.org/10.1007/s00170-010-3122-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-010-3122-4

Keywords

Navigation