Due to the intricacy of machining processes and inconsistency in material properties, analytical models are often unable to describe the mechanics of machining of carbon fiber reinforced polymer (CFRP) composites. Recently, soft computing techniques are used as alternate modeling and analyzing methods, which are usually robust and capable of yielding comprehensive, precise, and unswerving solutions. In this paper, drilling experiments as per the Taguchi L27 experimental layout are carried out on bi-directional carbon fiber reinforced polymer (BD CFRP) composite laminates using three types of drilling tools: high speed steel (HSS), uncoated solid carbide (USC) and titanium nitride coated SC (TiN-SC). The focus of this work is to determine the best drilling tool that produces good quality drilled holes in BD CFRP composite laminates. This paper proposes a novel prediction model 'genetic algorithm optimised multi-layer perceptron neural network' (GA-MLPNN) in which genetic algorithm (GA) is integrated with Multi-Layer Perceptron Neural Network. The performance capability of response surface methodology (RSM) and GA-MLPNN in prediction of thrust force is investigated. RSM is also used to evaluate the influence of process parameters (spindle speed, feed rate, point angle and drill diameter) on thrust force. GA is used to optimize the thrust force and its optimization performance is compared with that of RSM. It is observed that the GA-MLPNN is better predicting tool than the RSM model. The investigation in this paper demonstrates that TiN-SC is the best tool for drilling BD CFRP composite laminates as minimum thrust force is developed during its use.
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