Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach

Tool wear monitoring is deemed as an essential technology of the intelligent manufacturing to guarantee the processing quality and improve the machining efficiency. In this paper, a prediction model based on adaptive particle swarm optimization (APSO) algorithm and least squares support vector machine (LS-SVM) algorithm is proposed for the recognition of drill wear. Cutting force signal and vibration signal are used for tool wear monitoring. And these signals are preprocessed through wavelet threshold de-noising algorithm. Multiple signal feature extraction methods are carried out to process the sample data related to drill wear status. The mean absolute error of the tool wear recognition model is 0.91%, better than the standard LS-SVM algorithm under the same condition.