Abstract
This study discusses the development of an Adaptive neuro–fuzzy inference system (ANFIS) model for determining the surface roughness (Ra) during machining of multidirectional woven fabric Carbon fiber reinforced plastics (CFRP) using Abrasive waterjet machining (AWM). Three variable input parameters—Jet pressure (JP), Traverse speed (TS), and Standoff distance (SOD)—were selected to assess the roughness of the CFRP along the traverse direction of the cut surface. The experimental results show that a lower JP deteriorated the finish by creating surface rupture. On the other hand, a poor surface finish was observed in the case of machining at higher TS and SOD. Further, the developed ANFIS model was used to validate the results and it was found that the predicted values were in good agreement with a 95 % confidence level. It was also evident that the ANFIS technique is helpful for better prediction of the experimental data with minimum error. Finally, the cut surface morphology was analyzed using a 3D non-contact surface profilometer and the results are discussed.
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