Abstract
Drilling is a hole making process on machine components at the time of assembly work, which are identify everywhere. In precise applications, quality and accuracy play a wide role. Nowadays’ industries suffer due to the cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a serious topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. The effect of alteration of drill geometry on thrust force and burr size of drilled hole was investigated by the Taguchi design of experiments and found an optimum combination of the most significant input parameters from ANOVA to get optimum reduction in terms of burr size by design expert software. Drill thrust influences more on burr size. The clearance angle of the drill bit causes variation in thrust. The burr height is observed in this study. These output results are compared with the neural network software @easy NN plus. Finally, it is concluded that by increasing the number of nodes the computational cost increases and the error in nueral network decreases. Good agreement was shown between the predictive model results and the experimental responses.
Highlights
Drilling operations usually produce burrs on both the entrance and the exit surfaces of the work piece
The review of past studies carried out in drilling using neural networks is described below: Sudhakaran (1999) proposed a neural network model to identify the effect of drill geometry, lip height and point angle on burr height in the drilling of aluminum 2024-T3
Drill diameter, feed, speed and machine time were used as input to Artificial Neural Network (ANN) model
Summary
Drilling operations usually produce burrs on both the entrance and the exit surfaces of the work piece. Sokolowski et al (1994) proposed a neural network model to predict the burr height by considering the effect of cutting velocity, feed, depth of cut, work piece material and exit angle on burr formation. Singh et al (2006) developed a neural network model to predict flank wear Various process parameters, such as speed, feed, thrust force, torque force, and drill diameter were considered as inputs and the corresponding maximum flank wear was measured. The network parameters, such as momentum coefficient, number of hidden layers, and learning coefficient were determined on trial and error. The tool life is obtained by summing up the total cutting time The results of this experiment were used in the development of a neural network model. The experiments were tested for 15 different conditions and out of which 12 exhibited an error of less than ± 0.7μm, showing considerable prediction capability
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