Discrimination of Grinding Wheel Surface by Means of Neural Network Technique

Studies on In-process Evaluation of Grinding Wheel Surface

Abstract

In this study, a new technique of in-process evaluation of the wheel surface is proposed. Five specified wheel surfaces are prepared as the references via the appropriate dressing procedure, and grinding sounds generated by these wheels are discrimi-nated by analyzing the dynamic frequency spectrum with a neural network technique. In the case of conventional vitrified-bonded alumina wheel, grinding sound can be identified under the optimum network configuration in such that learning rate is 0.0029 and number of hidden layer is 420. This system can recognize instantaneously the difference of the wheel surface in a good degree of accuracy insofar as the dressing conditions are relatively widely changed. In addition, the network perceives the wheel wear because the grain tips are flattened as grinding proceeds and the grinding sound resembles to that of the wheel generated with lower dressing feed.