Tool cutting state recognition technology based on machining data characteristics

Abstract

Tool cutting as the core process of machine tool processing, cutting parameters, tool and parts material, processing procedures and other conditions will directly affect the cutting state of the tool, In order to solve the problem that the tool wear is too fast and the service life is too short, the cutting state of the tool can not be known in time during high-speed milling, a cutting state recognition technology based on spindle vibration signal and machining parameters of the machine tool was proposed. By analyzing the vibration signals and machining parameters of machine tool spindle under different conditions, combining with the methods of feature extraction and feature dimension reduction, the state recognition of cutting tool is completed. Finally, through experiments, the spindle vibration signal of vertical machining center machine tool was collected for feature vector extraction, and the comparison between the original feature vector and the actual value was obtained by dimensionality reduction to predict the cutting state of the tool. The results show that the proposed method has higher accuracy and recognition, and can recognize the cutting state of the cutting tool when cutting parts.