Wear monitoring of single-point dresser in dry dressing operation based on neural models
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The monitoring of different machining processes has been studied for years, however many processes still do not have a final solution for their controls. The dressing, as it is of great importance in the finishing of workpieces produced through the grinding, is an operation whose monitoring becomes necessary. In order to make the dressing automation and, in this case, the process of dresser exchange, there is a need for efficient and lowcost monitoring. The vibration sensor has great potential, but it is still little used for this purpose. In this work the vibration sensor and neural models were used to classify the wear of dressing tools for three different conditions. Dry dressing tests and data acquisition were performed in a surface-grinding machine. The raw signals were further filtered in different frequency bands. Then, two statistics were computed, which served as inputs to the neural models. The results were quite satisfactory for some models.
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