Milling force mixed-signal denoising based on ICA in high speed micro-milling

In order to obtain the real milling force signal in high speed micro-milling process, and accurately identify each exciting source, this paper studies on micro-milling force mixed-signal separation and identification technology based on combination of independent component analysis (ICA) and fast Fourier transform (FFT). The ICA method from theory of blind source separation combining is used with FFT to separate and identify the micro-milling force mixed-signal collected from the dynamometer, and the independent micro-milling force signal and noise signals are extracted. ICA theory¹ shows this method is suitable to separate both Gaussian signals and non-Gaussian signals. ICA could make up for the shortcomings of traditional methods which can only inhibit Gaussian noise signals. Using this method, the experiments successfully separate micro-milling force signal, non-Gaussian machining noise signal and Gaussian environmental noise signal.