Tool wear has long been identified as the most undesirable characteristic of the machining operations. Flank wear, is particular directly affects the work piece dimensions and the surface quality. A reliable and sensitive technique for monitoring the tool wears with out interrupting the process, is curtail in realization of the modern manufacturing concepts like unmanned machining centers, adaptive control optimization, etc. in this work an opto-electronic sensor is used in conjunction with a multilayered neural network for predicting the flank wear on the cutting tool.
The gap sensing system consists of a bifurcated optical fiber laser source and a photodiode circuit. The output of the photodiode circuit is amplified and converted to the digital from using an A/D converter. The digitized sensor signal along with the cutting parameters from the input to a three layered, feed forward, fully connected neural network. The neural network, trained off-line using a back propagation algorithm and the experimental data, is used to predict flank wear..