A Statistical Approach for Predicting and Optimizing Depth of Cut in AWJ Machining for 6063-T6 Al Alloy

Authors: Farhad Kolahan, A. Hamid Khajavi

Abstract:

In this paper, a set of experimental data has been used to assess the influence of abrasive water jet (AWJ) process parameters in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. The effects of these input parameters are studied on depth of cut (h); one of most important characteristics of AWJ. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the AWJ process parameters. The objective is to determine a suitable set of process parameters that can produce a desired depth of cut, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.

Keywords: AWJ machining, Mathematical modeling, Simulated Annealing, Optimization

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078176

References:

[1] M. Hashish, "A model for abrasive water jet (AWJ) machining", Transactions of ASME J. Eng. Mater. Technol., vol.111, no.2, pp.15 4-162, 1989.
[2] M.A. Azmir, A.K. Ahsan, "Investigation on glass/epoxy composite surfaces machined by abrasive water jet machining", J. Mater. Process. Techno., vol.198, pp.122-128, 2008.
[3] C. Ma, R.T. Deam, "A correlation for predicting the kerf profile from abrasive water jet cutting", Experimental Thermal and Fluid Science, vol.30, pp.337-343, 2006.
[4] J. John Rozario Jegaraj, N. Ramesh Babu, "A soft computing approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear", J. Mater. Process. Techno., vol.185, no.1-3, pp.217-227, 2007.
[5] M. Hashish, "Optimization factors in abrasive waterjet machining", ASME J. Eng. Ind., vol.113, pp.29-37, 1991.
[6] R. Kovacevic, M. Fang, "Modeling of the influence of the abrasive waterjet cutting parameters on the depth of cut based on fuzzy rules", Int. J. Mach. Tools Manuf., vol. 34, no.1, pp.55- 72, 1994.
[7] M. Hashish, "A modeling study of metal cutting with abrasive water jets", Transactions of ASME J. Eng. Mater. Technol., vol.106, no.1, pp.88-100, 1984.
[8] M. Ramulu, D. Arola, "The influence of abrasive waterjet cutting conditions on the surface quality of graphite/epoxy laminates", Int. J. Mach. Tools Manuf., vol.34, no.3, pp.295- 313, 1994.
[9] P.S. Chakravarthy, N. Ramesh Babu, "A hybrid approach for selection of optimal process parameters in abrasive water jet cutting", in, Proceedings of the Institution of Mechanical Engineers, Part B: J. Eng. Manuf., vol.214, pp.781-791, 2000.
[10] D.S. Srinivasu, N. Ramesh Babu, "A neuro-genetic approach for selection of process parameters in abrasive water jet cutting considering variation in diameter of focusing nozzle", Appl. Soft Comp., Vol.8, pp.809-819, 2008,.
[11] D.C. Montgomery, E.A. Peck, G.G. Vining, Introduction to Linear Regression Analysis. third ed., Wiley, New York, 2003.
[12] Kirkpatrick, S., Gelatt, C. & Vecchi, M. "Optimization by simulated annealing". Science, vol.220, pp.671-680, 1983.