Extraction Feature of Spindle Unbalance of Machine Tool Based on the Wavelet Transform

A new method for extracting spectrum feature of spindle unbalance of machine tool is proposed. The flatness error of workpiece surface includes much errors information, and the information contains high frequency signal and low frequency signal. For these errors information, a new identification method of turning errors of workpiece based on the wavelet transform and power spectral density analysis is proposed. According to the focal variation character of wavelet and the energy value of power spectral density analysis, the feature of spindle unbalance from the measured flatness error of workpiece is extracted and identified.