Skip to main content
SpringerLink
Log in

Parameter design for cut surface characteristics in abrasive waterjet cutting of Al/SiC/Al2O3 composite using grey theory based RSM

  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The abrasive mixed waterjet was successfully employed to cut many materials including austenitic steel, inconel and glass for a variety of industrial applications. The present work focusses on studying the surface roughness, striation zone and striation angle in Abrasive waterjet cutting (AWJC) of Al/SiC/Al2O3 composite. The water pressure, traverse speed, abrasive flow rate and stand-off distance were included as the dominant parameters in the study. The features of striation zone (length and angle) and surface roughness were observed as the responses for each of the cutting trials planned as per Taguchi’s L18 orthogonal array. Parameter design was performed using the grey theory based response surface methodology (g-RSM) by following the method of simultaneous optimization to forecast the optimal cutting condition. All the studied parameters and their interactions were found to have a substantial effect on the observed responses. Significant improvements were observed in the responses obtained with the optimal parameter setting predicted by the g-RSM approach. The Atomic force microscopy (AFM) images and P-profile plots were also studied to observe the texture of the cut surface.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. Wang and W. C. K. Wong, A study of waterjet cutting of metallic coated sheet steels, International Journal of Machine Tools and Manufacture, 39 (6) (1999) 855–870.

    Article  Google Scholar 

  2. M. Kantha Babu and O. V. Krishnaiah Chetty, Studies on recharging of abrasives in abrasive water jet machining, International Journal of Advanced Manufacturing Technology, 19 (9) (2002) 697–703.

    Article  Google Scholar 

  3. A. Akkurt, M. K. Kulekci, U. Seker and F. Ercan, Effect of feed rate on surface roughness in abrasive waterjet cutting applications, Journal of Materials Processing Technology, 147 (3) (2004) 389–396.

    Article  Google Scholar 

  4. J. Wang, T. Kuriyagawa and C. Z. Huang, An experimental study to enhance the cutting performance in abrasive waterjet machining, Machining Science and Technology, 7 (2) (2003) 191–207.

    Article  Google Scholar 

  5. L. M. Hlavac, I. M. Hlavacova, L. Gembalova, J. Kalicinsky, S. Fabian, J. Mestanek, J. Kmec and V. Madr, Experimental method for the investigation of the abrasive water jet cutting quality, Journal of Materials Processing Technology, 209 (20) (2009) 6190–6195.

    Article  Google Scholar 

  6. G. Cosansu and C. Cogun, An investigation on use of colemanite powder as abrasive in abrasive waterjet cutting (AWJC), Journal of Mechanical Science and Technology, 26 (8) (2012) 2371–2380.

    Article  Google Scholar 

  7. K. Krishnaiah, P. Shahabudeen and R. Jeyapaul, Quality management research by considering multi-response problems in the Taguchi method-a review, International Journal of Advanced Manufacturing Technology, 26 (11-12) (2005) 1331–1337.

    Article  Google Scholar 

  8. J. Kechagias, G. Petropoulos and N. Vaxevanidis, Application of Taguchi design for quality characterization of abrasive water jet machining of TRIP sheet steels, International Journal of Advanced Manufacturing Technology, 62 (5-8) (2012) 635–643.

    Article  Google Scholar 

  9. S. Kumanan, J. E. R. Dhas and K. Gowtha, Determination of submerged arc welding process parameters using Taguchi method and regression analysis, Indian Journal of Engineering and Materials Sciences, 14 (3) (2007) 177–183.

    Google Scholar 

  10. P. J. Parikh and S. S. Lam, Parameter estimation for abrasive water jet machining process using neural networks, International Journal of Advanced Manufacturing Technology, 40 (5-6) (2009) 497–502.

    Article  Google Scholar 

  11. J. E. R. Dhas and K. Somasundaram, Weld residual stress prediction using artificial neural network and Fuzzy logic modelling, Indian Journal of Engineering and Materials Sciences, 18 (5) (2011) 351–360.

    Google Scholar 

  12. P. S. Chakravarthy and N. R. Babu, A new approach for selection of optimal process parameters in abrasive water jet cutting, Materials and Manufacturing Processes, 14 (4) (1999) 581–600.

    Article  Google Scholar 

  13. P. R. Vundavilli, M. B. Parappagoudar, S. P. Kodali and S. Benguluri, Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process, Knowledge-Based Systems, 27 (2011) 456–464.

    Article  Google Scholar 

  14. C. Felix Prasad, S. Jayabal and U. Natarajan, Optimization of tool wear in turning using genetic algorithm, Indian Journal of Engineering and Materials Sciences, 14 (6) (2007) 403–407.

    Google Scholar 

  15. I. Karakurt, G. Aydin and K. Aydiner, Analysis of the kerf angle of the granite machined by abrasive waterjet (AWJ), Indian Journal of Engineering and Materials Sciences, 18 (6) (2011) 435–442.

    Google Scholar 

  16. P. Vijian, V. P. Arunachalam and S. Charles, Study of surface roughness in squeeze casting LM6 aluminium alloy using Taguchi method, Indian Journal of Engineering and Materials Sciences, 14 (1) (2007) 7–11.

    Google Scholar 

  17. L. Singh, R. A. Khan and M. L. Aggarwal, Empirical modeling of shot peening parameters for welded austenitic stainless steel using grey relational analysis, Journal of Mechanical Science and Technology, 26 (6) (2012) 1731–1739.

    Article  Google Scholar 

  18. R. Adalarasan, M. Santhanakumar and A. Shanmuga sundaram, Optimization of weld characteristics of friction welded AA 6061-AA 6351 joints using grey-principal component analysis (G-PCA), Journal of Mechanical Science and Technology, 28 (1) (2014) 301–307.

    Article  Google Scholar 

  19. N. Muhammad, Y. H. P. Manurung, M. Hafidzi, S. K. Abas, G. Tham and E. Haruman, Optimization and modeling of spot welding parameters with simultaneous multiple response consideration using multi-objective Taguchi method and RSM, Journal of Mechanical Science and Technology, 26 (8) (2012) 2365–2370.

    Article  Google Scholar 

  20. R. Ramakrishnan and R. Arumugam, Optimization of operating parameters and performance evaluation of forced draft cooling tower using response surface methodology (RSM) and artificial neural network (ANN), Journal of Mechanical Science and Technology, 26 (5) (2012) 1643–1650.

    Article  Google Scholar 

  21. G. Ren, S. Heo, T. H. Kim and C. Cheong, Response surface method-based optimization of the shroud of an axial cooling fan for high performance and low noise, Journal of Mechanical Science and Technology, 27 (1) (2013) 33–42.

    Article  Google Scholar 

  22. R. Adalarasan, M. Santhanakumar and M. Rajmohan, Application of grey Taguchi based response surface methodology (GT-RSM) for optimizing the plasma arc cutting parameters of 304L stainless steel, International Journal of Advanced Manufacturing Technology, 78 (2015) 1161–1170.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Saveetha Engineering College, Chennai, 602105, Tamilnadu, India

    M. Santhanakumar & R. Adalarasan

  2. Department of Industrial Engineering, College of Engineering Guindy, Anna University, Chennai, 602105, Tamilnadu, India

    M. Rajmohan

Authors
  1. M. Santhanakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Adalarasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Rajmohan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Santhanakumar.

Additional information

M. Santhanakumar received his M.E degree in Industrial Engineering from the College of Engineering, Anna University in 2008 and currently works as an Assistant Professor in the Department of Mechanical Engineering at Saveetha Engineering College, Chennai, India. He has published more than 20 articles in international journals and international conferences. His general research interests include Abrasive machining processes, multi response optimization and operations research.

R. Adalarasan received his M.E degree in Production Engineering from the Faculty of Engineering and Technology, Annamalai University in 2000 and currently works as an Associate Professor in the Department of Mechanical Engineering at Saveetha Engineering College, Chennai, India. He has authored more than 20 papers published in international journals and international conferences. His research interests include welding processes, MMCs, optimization and meta-heuristics.

M. Rajmohan is currently serving as Associate Professor in the Department of Industrial Engineering, College of Engineering, Guindy, Anna University, Chennai, India. He received both his Master of Engineering and Doctoral degrees in Industrial Engineering from the same University. His areas of research include Logistics and Supply Chain Management, Applied Design of Experiments, Data Analytics and Multi Criteria Decision Making.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santhanakumar, M., Adalarasan, R. & Rajmohan, M. Parameter design for cut surface characteristics in abrasive waterjet cutting of Al/SiC/Al2O3 composite using grey theory based RSM. J Mech Sci Technol 30, 371–379 (2016). https://doi.org/10.1007/s12206-015-1242-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-015-1242-3

Keywords

Search

Navigation