A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion

•A data-driven tool wear recognition and prediction approach is proposed.•A radar map feature fusion method is proposed to obtain tool health indicator.•The Adaboost-DT is developed for tool wear state recognition.•The SBiLSTM enables quantitative prediction of tool wear with limited data input. Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A data-driven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes.