On tool wear estimation through neural networks

During metal cutting operations as machining progresses and the tool wears out, the surface quality and the dimensional accuracy of the product degrade. In our work we are developing a neural network based indirect method, for continuous estimation of tool flank wear for end milling operations. We present here the simulation results of a performance driven design study, of the recurrent part of the network proposed by Kamarthi et al. (1995). The aim of this study is to improve the performance of the data fusion algorithm to generate more accurate final flank wear estimates. Testing of the recurrent network has proved its ability to properly integrate the first level flank wear estimates into a reliable final flank wear estimate. Using an architecture with one delayed output and one additional delayed input vector improves the performance of the network.