基于Attention-LSTM的CNC刀具破损在线检测系统

为了在数控（CNC）机床批量加工过程中对刀具破损进行检测以减少残次产品，提出一种利用机床主轴功率信息，基于Attention-LSTM的CNC生产线刀具破损在线监测方法。该方法以数控系统内置传感器作为数据源获取机床主轴功率时间序列，在数据采集环节中，需要分辨加工过程中的不同工序以及该工序所使用的刀具编号。因此，在数据采集环节中，同时对数控代码和主轴功率进行采集，使用数控代码解析方式对采集数据进行处理，完成加工过程的工序识别，再使用Attention-LSTM算法对主轴功率数据进行预测，然后利用DTW算法计算时间序列相似度。加工过程功率时间序列和标准时间序列之间的相似程度应当处于合理的阈值范围内，否则认为此次加工过程中发生刀具破损。以FANUC数控系统为平台进行实验，验证了刀具破损识别的准确率。

On-line Detection System of CNC Tool Breakage Based on Attention-LSTM

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.