A Tool Wear Monitoring Method Based on WOA-DBO-SVM for Small-Deep Hole Drilling

Drilling is the most basic mechanical manufacturing process, and deep hole drilling technology is widely used in aerospace, weapons, medical electronics engineering machinery manufacturing, and other fields. Aiming at the engineering requirements of deep hole drilling tool wear status detection, a drill wear status recognition method based on drilling acoustic and vibration signals is proposed. According to the acoustic signals and vibration signals of drills with different wear levels in the drilling process, the time-domain characteristics, frequency-domain characteristics, time-frequency characteristics, and psychoacoustic parameter characteristics of the acoustic and vibration signals are extracted, and the whale optimization algorithm (WOA) is used to optimize the characteristics; Reuse the Dung Beetle Optimizer Algorithm (DBO) to optimize the parameters of the Support Vector Machine (SVM), and use SVM to achieve tool wear state recognition based on sound and vibration signal features. The experimental results show a nonlinear coupling relationship between the wear degree of small deep-hole drill bits and the characteristics of drilling sound and vibration signals. Using the SVM algorithm optimized by DBO, based on the selected sound and vibration characteristics, the wear state of drilling tools can be accurately identified, and the recognition accuracy can reach 98.148%.