Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear |
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Authors: | Saurabh Garg Surjya K. Pal Debabrata Chakraborty |
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Affiliation: | (1) Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India;(2) Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721 302, West Bengal, India |
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Abstract: | This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice. |
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Keywords: | Backpropagation neural network Radial basis function network Flank wear Drilling |
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