Abstract: In order to detect tool damage during the batch processing of CNC machine tools to reduce defective products, an online monitoring method for tool damage in CNC production lines based on Attention-LSTM using machine tool spindle power information is proposed. The method uses the built-in sensor of the numerical control system as the data source to obtain the time series of the spindle power of the machine tool. In the data collection link, it is necessary to distinguish the different processes in the machining process and the tool number used in the process. Therefore, in the data acquisition link, the NC code and spindle power are collected at the same time, the collected data is processed by the NC code analysis method, the process identification of the processing process is completed, the Attention-LSTM algorithm is used to predict the spindle power data, and then the DTW algorithm is used to calculate the time series similarity. The degree of similarity between the processing power time series and the standard time series should be within a reasonable threshold range, otherwise it is considered that tool breakage occurred during the processing. Experiments were conducted on the FANUC CNC system to verify the accuracy of tool breakage recognition.

Key words: online detection, spindle power, tool failure, attention mechanism