This chapter presents a detailed study of abrasive water jet (AWJ) cutting of thin and thick Kevlar fiber-reinforced polymer (FRP) composites used in transport aircraft and anti-ballistic applications. Kevlar composites are considered to be very challenging to machine using traditional techniques. Most of the research conducted in the area of AWJ cutting has been limited to single response optimization. However, in real life machining, the performance of a process/product demands multi-objective optimization (MOO). No work has been reported till now using different MOO techniques for AWJ cutting of Kevlar FRP composites. Experimental modeling of depth of cut and various design of experiments based single and multi-objective optimization studies are presented here. Statistical analysis of variance has been performed to rank the different process parameters and estimate their effects on various AWJ cut kerf quality characteristics. The studies conducted in this chapter are likely to prove beneficial to the AWJ community in performing modeling and simultaneous optimization of multiple quality characteristics.
Multi response prediction of cutting force and delamination in carbon fiber reinforced polymer using backpropagation neural network-genetic algorithm
