Abstract

Glass fiber reinforced polymer composites are finding its increased applications in variety of engineering applications such as aerospace, automobile, electronics and other industries. However, the users of FRP are facing difficulties to machine it, because of fiber delamination, fiber pull out, short tool life, matrix debonding and formation of powder like chips. The present investigation focuses on the optimization of process parameters for surface roughness of glass fiber reinforced polymer (GFRP) composites using Genetic Algorithm (GA). Experiments are conducted based on the established Taguchi's L 25 orthogonal array in Design of Experiments (DOE) on an all-geared lathe using poly-crystalline diamond (PCD) tool. The process parameters considered were cutting speed, feed, depth of cut, and work piece (fiber orientation angle). A second order mathematical model was developed for surface roughness prediction using Response Surface Methodology (RSM). An attempt has also been made to optimize the surface roughness prediction model coupling with Genetic Algorithm (GA) to optimize the objective function. Validation of the optimized results was also performed by confirmation experiments.

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Glass fiber reinforced polymer (GFRP) composites are finding increased applications due to their superior advantages over other engineering materials. This paper presents, the use of Fuzzy logic combined with Taguchi method for the optimization of multiple performance characteristics considering surface roughness, and cutting force. Experiments were planned according to L 25 orthogonal array in Taguchi's design of experiments. Experiments were performed on an all geared lathe using CBN (Cubic Born Nitride) cutting tool insert. The results reveal that the optimization technique is greatly helpful for simultaneous optimization of multiple quality characteristics i.e., surface roughness (R a ) and cutting force (F z ).