Abstract
In this study, a new technique of in-process evaluation of a grinding wheel surface is proposed. Some specific wheel surfaces are prepared as references by the appropriate truing and/or dressing procedure, and grinding sounds generated by these wheels are discriminated by analyzing the dynamic frequency spectrum by a neural network technique. In the case of a conventional vitrified-bonded alumina wheel, the grinding sound can be identified in the optimum network configuration. Therefore, this system can instantaneously recognize differences in the wheel surface with a good degree of accuracy insofar as the wheel conditions are relatively widely changed. In addition, the network can perceive wheel wear because the grain tips are flattened as grinding proceeds and the grinding sound becomes similar to that of a wheel generated with lower dressing feed. The resinoid-bonded cubic boron nitride (CBN) wheel is also discriminable when a grinding sound in a higher frequency range is analyzed.
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