DESIGN OF ARTIFICIAL NEURAL NETWORKS FOR TOOL WEAR MONITORING

An on-line scheme for tool wear monitoring using artificial neural net works (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analyti cal model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool w ear at a given instance of time depends on the tool wear value at a pr evious instance of time, memory is included in the ANN. ANNs without m emory, with one-phase memory, and with two-phase memory are investigat ed in this study. The effect of various training parameters, such as l earning coefficient, momentum, temperature, and number of hidden neuro ns, on these architectures is studied. The findings and experience obt ained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identificati on in intelligent manufacturing.