Experimental Investigation, Modelling and Comparison of Kerfwidth in Laser Cutting of GFRP

Day by day use of composite materials increases due to their superior strength to weight ratio and stiffness to weight ratio at high service temperatures. Aeronautic, aerospace, automotive and marine industry are the dominant consumers of the composites, but their properties like brittleness, anisotropy and non-homogeneity make it a difficult to machine by conventional machining methods. This leads to study the machinability characteristics of composites. Laser machining offers an attractive machinability as an alternative for machining the composites. The present investigation deals with the laser machining of the Glass Fibre Reinforced Plastic (GFRP) Composite. Experiments were performed based on Taguchi L27 orthogonal array in order to investigate the effect of laser cutting parameters: Laser Power, Cutting Speed and Gas Pressure on cut quality parameter Kerfwidth. Based on the experimental results, Second Order Regression, Artificial Neural Network (ANN) and Fuzzy Logic (FL) based predictive models have been developed. Then an attempt is made to compare the results of statistical technique with computational technique. After comparing the experimental results and the predicted results it is found that the data for each response are well fitted in the developed models and these models can be used for predicting the kerfwidth within the specific range of inputs for a given machine tool with more than 95 % accuracy.