A hybrid information model based on long short-term memory network for tool condition monitoring

Excessive tool wear leads to the damage and eventual breakage of the tool, workpiece, and machining center. Therefore, it is crucial to monitor the condition of tools during processing so that appropriate actions can be taken to prevent catastrophic tool failure. This paper presents a hybrid information system based on a long short-term memory network (LSTM) for tool wear prediction. First, a stacked LSTM is used to extract the abstract and deep features contained within the multi-sensor time series. Subsequently, the temporal features extracted are combined with process information to form a new input vector. Finally, a nonlinear regression model is designed to predict tool wear based on the new input vector. The proposed method is validated on both NASA Ames milling data set and the 2010 PHM Data Challenge data set. Results show the outstanding performance of the hybrid information model in tool wear prediction, especially when the experiments are run under various operating conditions.