Tool Wear Monitoring using NNge Algorithm

Cutting tools are required to do the necessary machining operations in the manufacturing Industry. Continuous machining operation causes the tool to degrade. The machining operation done using a worn-out tool will have a poor surface finish. The poor surface finish undermines the accuracy of the component. This can be solved by using online system condition monitoring tools which helps in reducing the tool maintenance cost all the while increasing the productivity. This paper presents the classification performance of the Nearest-neighbour-like algorithm using non-nested generalized exemplar (NNge). A set of statistical data extracted from vibration signals for both good and faulty conditions form the input to the algorithm. In the present study, the NNge algorithm is able to achieve 100% classification accuracy.