Dynamic milling forces have been widely used to monitor the condition of the milling process. However, it is very difficult to measure milling forces directly in operation, particularly in the industrial scene. In this paper, a dynamic force identification method in time domain, conjugate gradient least square (CGLS), is employed for reconstructing the time history of milling forces using acceleration signals in the peripheral milling process. CGLS is adopted for force identification because of its high accuracy and efficiency, which handles the ill-conditioned matrix well. In the milling process, the tool with high-speed rotation has different transfer functions between tool nose and accelerometers at different angular positions. Based on this fact, the averaged transfer functions are employed to reduce the error amplification of regularization processing for milling force identification. Moreover, in order to eliminate the effect of idling and high-frequency components on identification accuracy, the Butterworth band-pass filter is adopted for acceleration signals preprocessing. Finally, the proposed method is validated by milling tests under different cutting parameters. Experimental results demonstrate that the identified and measured milling forces are in good agreement on the whole time domain, which verifies the effectiveness and generalization of the indirect method for milling force measuring. In addition, the Tikhonov regularization method is also implemented for comparison, which shows that CGLS has higher accuracy and efficiency.
Bayesian sparse regularization for multiple force identification and location in time domain