Method for extracting state discrimination characteristic parameter of metal drawing piece

Abstract

The invention discloses a method for extracting a state discrimination characteristic parameter of a metal drawing piece, comprising the following steps of: setting acoustic emission sensors at the edge of a metal workpiece and the edge of a terrace die for acquiring acoustic emission signals at different parts and transmitting the acoustic emission signals to a computer and carrying out the normalization processing on the signal in the computer; carrying out the wavelet-packet decomposition on the signal subjected to the normalization processing and reconstructing the signal in a frequency range to obtain a signal containing frequency components of a metal crack acoustic emission signal; and finally, establishing an autoregression model of a metal crack acoustic emission signal frequency range, calculating an autoregression spectrum according to autoregression model parameters, extracting accurate energy value of the acoustic emission signal frequency range according to a formula and using the energy value as the characteristic parameter for reflecting the metal crack characteristic. The invention improves the accuracy for parameter identification and state prediction of the metal drawing state discrimination, greatly reduces the sampling quantity and is suitable for high-requirement occasions for monitoring short-time pulse signals on line.

Description

technical field [0001] The invention relates to a method for state detection and fault diagnosis of metal parts based on acoustic emission technology, in particular to a method for extracting characteristic parameters for state discrimination of metal parts in a deep-drawing state. Background technique [0002] Deep drawing is a stamping process method in which a flat blank is used to support an open hollow part with a special mold. The thin-walled hollow part is manufactured by deep drawing, which has high production efficiency, saves materials, good strength and rigidity of the part, and high precision. The range of deep processing is very wide, from small parts with a diameter of a few millimeters to large parts with a diameter of 2~3m. Therefore, deep drawing plays a very important role in industrial sectors such as automobiles, aerospace, defense, electrical appliances and electronics, as well as in the production of daily necessities. [0003] In the processing inspect...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: a large amount of data needs to be collected, and it is difficult to realize on-site real-time online detection; and the friction acoustic emission signal generated by the plastic deformation of the metal and the acoustic emission signal generated by the plastic deformation of the workpiece are mixed in the deep drawing process of the metal sheet. Together, the acoustic emission signal generated by the friction between the workpiece and the mold is very similar to the acoustic emission signal generated by the early microcracks of the workpiece, and its generation mechanism is also the same. They are all due to dislocation slip, crack formation and fracture, which are very difficult to distinguish

The disadvantage of this method is that it is assumed that the characteristic parameters obey the normal distribution and lack certain accuracy, which will bring errors to the subsequent identification.

In addition, the acoustic emission signal generated by the metal material during the deep drawing process is a short-term pulse signal, and some peaks will be missing in the measured waveform data, or when there is a small abnormal component in the local signal, the judgment is difficult to achieve

[0007] Therefore, the above two methods cannot accurately describe the state of the stretched part

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