Study on tool wear state recognition algorithm based on spindle vibration signals collected by homemade tool condition monitoring ring

With a view to further realising the intelligence of tool condition monitoring (TCM) and to address the high cost and low stiffness problems of existing smart tool holders, this study firstly proposed a tool condition monitoring ring, which can be mounted on the tool shank and is easily disassembled and reused. This ring acquires the triaxial vibration signals of the spindle. This study also firstly applied manifold learning to TCM model for data dimensionality reduction and proposed a weighted random forest based on K-nearest neighbour (KWRF). A tool wear state recognition algorithm based on wavelet threshold de-noising (WTD), variational mode decomposition (VMD), manifold learning and KWRF was proposed. Four sets of full life cycle milling experiments were carried out on 45 steel with cemented carbide milling inserts. The results showed that the recognition accuracy of the proposed model reached 98.74%, which verified the effectiveness of the proposed model.