Abstract
The present work deals with developing a fuzzy back-propagation neural network scheme for the prediction of drill wear. Drill wear is an important issue in manufacturing industries, which not only affects the surface roughness of the hole but also influences drill life. Therefore, replacement of a drill at an appropriate time is of significant importance. Flank wear in a drill depends upon the input parameters like speed, feedrate, drill diameter, thrust force, torque and chip thickness. Therefore, it sometimes becomes difficult to have a quantitative measurement of all the parameters and a qualitative description becomes easier. For these kinds of situations, a fuzzy back-propagation neural network model was trained and was shown to predict drill wear with reasonable accuracy. In the present case, a left and right (LR)-type fuzzy neuron was used. The proposed model is composed of various modules like fuzzy data collection at input fuzzy neuron, defuzzification of input data to get output, calculation of mean square error (MSE), and feedback to update the network. Results show a very good prediction of drill wear from the fuzzy back-propagation neural network model.
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