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The technology of waterjet machining employs either a pure jet of water or water mixed with abrasives to cut a variety of materials including titanium, steel, brass, aluminium, stone and Inconel. Both formats with good machining capabilities are characterized by the absence of thermal distortion and micro cracking. The process limitation includes a noisy environment and an untidy workplace (Wang and Wong, 1999). In abrasive waterjet cutting (AWJC), the hydrostatic energy of pressurized water is transformed into enormous kinetic energy of the jet, which is focused through a small orifice at supersonic speeds to perform the desired machining operation (Kulekci, 2002). A good abrasive performance is an important criterion, as the cost of abrasive is over weighed by the higher cutting speeds achieved with a better performing abrasive (Khan and Haque, 2007). The depth of penetration of the abrasive waterjet can be improved by an optimal amount of recharging of abrasives (Kantha Babu and Krishnaiah Chetty, 2002). The AWJC parameters like water pressure, traverse rate and abrasive flow rate are identified to influence the quality characteristics of the cut surface including kerf width and surface roughness. Experimental results have proved that higher traverse rate and jet impact angle are associated with an increase in material removal rate, surface roughness and waviness values (Wang et al., 2003). The surface finish obtained with Al6061 alloy is better than that observed with pure aluminium in AWJC applications. The elements present in Al6061 alloy are significant for obtaining a better surface finish (Akkurt et al., 2004).
The study of relationship between the parameters and responses is essential to enable quality control in AWJC (Hlavac et al., 2009). While cutting aramid fibre reinforced plastics, traverse feed rate is observed to be more significant than the abrasive flow rate in affecting the surface roughness and kerf taper ratio. A higher kinetic energy of water can produce a better cut quality (Azmir et al., 2009). From the literature, it is understood that the input parameters like stand-off distance, feed rate, flow rate of abrasives and water pressure play an important role in influencing the process responses in AWJC.
The multi response optimization problems can be handled by methods like grey relational analysis (GRA), genetic algorithm (GA), artificial neural network (ANN), response surface methodology (RSM), fuzzy logic, principal component analysis (PCA) and data envelopment analysis (Krishnaiah and Shahabudeen, 2012). Models are available to relate the kerf profile with cutting speed to produce straighter cuts (Ma and Deam, 2006). The nozzle oscillation at smaller angles is found to improve the depth of jet penetration in alumina with proper selection of AWJC parameters (Wang, 2007). LS-DYNA (software) is used to simulate the process of AWJC. The relationship between the processing parameters and the cutting depth is validated using the experimental data (Wenjun et al., 2011).