Session 3 Computer and Computational Intelligence
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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, feed rate, 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. In those cases, a fuzzy neural network based prediction model becomes more useful in tool condition monitoring (TCM). This paper describes the application of such a fuzzy neural network model in prediction of drill wear. Here chip thickness has been expressed as fuzzy linguistic variable to be used as an input to the artificial neural network (ANN). Fuzzy Back Propagation Neural Network (FBPNN) and Fuzzy Self-Organizing Feature Map (FSOFM) networks have been tried to develop drill wear prediction system in drilling a mild steel work piece. Finally comparative performances of different TCM strategies along with different ANN architectures tried in the present work for development of a robust drill wear prediction systems.