Real time FFT identification based time-varying chatter frequency mitigation in thin-wall workpiece milling

Due to the detrimental effect on tool life, material removal efficiency, and workpiece surface equality, chatter has become a major obstacle to high-performance milling. According to the previous research, the active noise equalizer (ANE) can decrease chatter frequencies with having a little influence on normal frequencies, which satisfies the initial control requirement for optimization of actuator performance. Recent researches show that chatter frequencies are time-varying with broadband characteristic in thin-walled component milling. However, tradition studies focused on invariant line spectrum chatter frequency suppression, which cannot meet the time-varied need. To this end, in this research, the real-time fast Fourier transform (FFT) identification is introduced into ANE, which can update the reference chatter frequencies for time-varying chatter frequencies’ suppression in thin-walled workpiece milling. However, during the period between two FFT, the identified frequencies remain unchanged, which will deviate from the real chatter frequencies and disable the controller. In order to deal with this problem, the modified narrowband filtered-x least mean square (NFXLMS) is combined with the real-time FFT identification for time-varying chatter frequencies suppression. The modified NFXLMS is a narrowband active control algorithm, which can handle the small change of chatter frequencies during the period between two FFT. Simulation results show that the proposed two control algorithms can both suppress the time-varying chatter frequencies effectively. The modified NFXLMS has better control effect than ANE, but it has larger impacts every time the chatter frequencies are identified by FFT. Finally, the thin-wall workpiece milling tests are implemented. The off-line simulation based on practical experimental data is carried out that indicates that the developed algorithms work well in practice.