A Neural Network Based Prediction Modeling for Machinability Characteristics of Zea Fiber-Polyester Composites

Abstract

Composites based on agricultural residues are extensively used in engineering applications because of high mechanical strength accompanied by the low weight factor. Drilling is the universally used machining process in automobile and structural industries. The drilling in polymeric composites is an unavoidable operation for facilitating the assembly parts due to the reason that gluing is quite complex and non metallic nature of materials. The objective of this study is to measure and analyze the cutting conditions which influence the thrust force, torque and delamination factor in drilling of the zea fiber reinforced polyester composites. The parameters considered are spindle speed, feed rate and drill bit diameter. The drilling experiments were performed based on a full factorial design of experiments and artificial neural network model was developed to predict the influence of cutting parameters on thrust force, torque and delamination factor.

A review on the machining of fiber-reinforced polymeric laminates

“…It has been reported that the ANN method accurately predicts the responses with respect to the confirmatory trials and the error is found to be less than 4%. 48…”

“…It has been reported that the ANN method accurately predicts the responses with respect to the confirmatory trials and the error is found to be less than 4%. 48 Fuzzy logic is another important technique used by the researchers for prediction of optimum parameters during machining. A study on the thrust force and torque during drilling of hybrid composites, it has been reported that the fuzzy system accurately predicts the optimum values of responses in comparison to the confirmatory trials.…”