Automatic milling fluttering alarming threshold value setting method based on 3 sigma rule

Abstract

The invention discloses an automatic milling fluttering alarming threshold value setting method based on the 3 sigma rule. The method comprises the steps that firstly, the state information in the milling process is obtained; secondly, forced vibration frequency filtering is conducted on signals, and fluttering sensitive frequency band filtering is conducted on the signals; thirdly, the obtained signals are subjected to feature extraction, and fluttering identification indexes are selected according to needs and calculated; fourthly, the indexes are subjected to the normal distribution check through the normal distribution ration assuming checking method; fifthly, after the normal distribution check, the threshold value section [mu-3 sigma, mu+3 sigma] is set according to the 3 sigma rule, wherein mu is the average value of the fluttering indexes, and the sigma is the standard deviation of the fluttering indexes; and sixthly, fluttering identification is conducted, and the time when continuous three points of the fluttering identification indexes exceed the threshold value section serves as the fluttering alarming time. The reliability of milling fluttering identification is high, and the misdiagnosis rate and the diagnosis missing rate are reduced.

Description

technical field [0001] The invention relates to the field of mechanical processing state monitoring, in particular to an automatic alarm threshold setting method for milling chatter. Background technique [0002] Milling technology has the advantages of high production efficiency, high processing accuracy and low processing cost, and is widely used in aviation, aerospace, mold, automobile and other manufacturing fields. Making full use of the advantages of advanced manufacturing technology depends largely on the ability to predict and control abnormal vibration phenomena (such as cutting chatter) in the milling process. During the milling process, due to the unreasonable selection of processing parameters, severe vibrations often occur between the tool and the workpiece, resulting in chatter. Chatter is a strong self-excited vibration between the tool and the workpiece in the metal cutting process. The occurrence of chatter not only reduces the surface quality and dimension...

AI Technical Summary

Problems solved by technology

Since the chatter identification index is susceptible to noise interference, there are false negatives and false positives in the identification of chatter by using the single index threshold method, and the state information of multi-feature domains in milling is not fully utilized, resulting in incomplete chatter detection information.

Finally, flutter is often within 0.1s from occurrence to formation, which requires consideration of the time complexity of the flutter identification algorithm, that is, the real-time problem

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