Drill wear feature identification under varying cutting conditions using vibration and cutting force signals and data mining techniques

Monitoring drill wear is a major topic in automated manufacturing operations. This paper presents an effective drill wear feature identification scheme based on robust clustering techniques. Three types of drill wear (namely; chisel wear, chipped edge and flank wear) are artificially induced on the drill point. The drill cutting edge wear related features are extracted experimentally by processing the force signals from a three-axis piezoelectric load cell in both the time and frequency domains. Techniques based on the short time Fourier transform (STFT), wavelet transform (WT) and statistical parameters are utilized for feature extraction. The sensitivity of the proposed method is tested under different cutting feed and speed conditions. The computational study is conducted using the features extracted from three dimensional vibration and cutting force signals. The type of drill wear and related variations in the cutting forces are identified using robust clustering methods. The objective is to isolate regions in the feature space, each region corresponding to one of the drill wear types. Results show that power spectral density data clusters better than data obtained using wavelet coefficients. The clustering results can be used to design classifiers for real time monitoring of wear conditions while drilling.