Milling chatter online detection method based on power spectral entropy difference

Abstract

The invention discloses a milling chatter online detection method based on the power spectral entropy difference. Through an acceleration sensor, vibration information in the milling process can be obtained; variation modal decomposition is used for decomposing a signal into a set of basic mode components, a basic mode component reconstruction signal of the high-frequency part is taken to obtain asignal of a frequency segment where a chatter component is located, the reconstruction signal is subjected to self-adaptation filtering, the power spectral entropy before and after signal filtering can be calculated, the obtained power spectral entropy is subjected to differencing to obtain the power spectral entropy difference, and the influence of filtering on chatter frequency band signal frequency spectrum distribution can be reflected. Compared with a traditional chatter detection method, the method can effectively separate chatter frequency segment signal, the threshold value reflects that influence of filtering on different state signals has the clear physical meaning, randomness of threshold selection can be avoided, accuracy and reliability of milling chatter detection can be improved, and the misjudgment rate and the misdetection rate can be reduced.

Description

technical field [0001] The invention belongs to the technical field of detection of machining state, and in particular relates to an online detection method of milling chatter based on power spectrum entropy difference. Background technique [0002] Milling is the most common machining method. It has the advantages of high efficiency, high precision, and low cost. It is widely used in manufacturing fields such as aviation, aerospace, molds, and automobiles. Chatter is a strong self-excited vibration in the metal cutting process, which will reduce the surface quality and size of the workpiece, reduce the pass rate of processing, and cause premature fatigue damage of machine tool parts, and aggravate tool wear. Choosing conservative processing parameters can avoid the outbreak of chatter, but reduces the processing efficiency. Although the analytical method can analyze the stability lobe diagram of milling and select the appropriate processing parameters based on this, due to...

AI Technical Summary

Problems solved by technology

During the process of milling from steady development to flutter, the signal evolves from a narrow-band distribution in the low-frequency band to a broadband distribution, and finally forms a narrow-band distribution in the high-frequency band, resulting in the common selection of the fundamental mode component with the largest normalized energy ratio as the sensitive fundamental mode component Doesn't accurately screen out flutter components early in flutter

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