Investigation and analysis of surface roughness in machining carbon fiber reinforced polymer composites using artificial intelligence techniques

Carbon fiber and its composites are increasingly used in many fields including defence, military, and allied industries. Also, surface quality is given due importance, as mating parts are used in machineries for their functioning. In this work, the turning process is considered for Carbon Fiber Reinforced Polymer (CFRP) composites by varying three important cutting variables: cutting speed, feed, and depth of cut. Correspondingly, the surface roughness is measured after the completion of turning operation. As well, a prediction model is created using different fuzzy logic membership function and Levenberg-Marquardt algorithm (LMA) in artificial intelligence. Later, the surface roughness values from the developed models are compared against the experimental values for its correlation and effectiveness in using different membership functions of fuzzy logic and ANN. Thus, the experimental results are analyzed using the effect graphs and it is presented in detail.