Feature Extraction from Sensor Data Streams for Optimizing Grinding Condition

摘要

A visualization method for time-series sensing data was designed to optimize grinding condition. The fluctuation pattern of time-series data streams can be visualized as a white and black pattern by utilizing the spindle power change rate average. The designed visualization method was applied to a condition monitoring in lapping operation. The relation between the fallout abrasive grain content and lapping behaviour was experimentally examined. In the lapping with grinding fluid containing no fallout abrasive, the spindle power decreased in a monotone manner with lapping time, while in the lapping with fallout abrasive, the spindle power decreased with lapping time up to 20s of lapping and then tended to converge on a constant value. The spindle power change rate average displayed as a white and black pattern reproduced the changes of spindle power very well. The appearance probability of white or black pattern has a strong relation with the fallout abrasive content and the designed data processing scheme could make possible to predict the grinding fluid condition from the easy-handling grinding test.