Fuzzy identification method for identifying cracks of deep drawn part

Abstract

The invention discloses a fuzzy identification method for identifying cracks of a deep drawn part, which comprises the following steps of: firstly preprocessing an acquired original acoustic emission signal; and then extracting a characteristic parameter of the preprocessed acoustic emission signal on the basis of a local wave time frequency domain local energy characteristic extraction method; carrying out automatic recombination by a genetic algorithm to generate an optimal characteristic parameter which is used as a characteristic parameter for identifying the cracks of the deep drawn part; if the optimal characteristic parameter does not comply with the normal distribution, converting the optimal characteristic parameter into a probability variable complying with the normal distribution; solving a possibility distribution function and corresponding possibility, as well as a membership function of the probability variable according to the probability consistency principle and the possibility theory, and finally carrying out fuzzy diagnosis. The fuzzy identification method eliminates the defects in state identification by using a traditional method in which the characteristic parameter is presumed to comply with the normal distribution, can accurately differentiate normal state from crack state, effectively improve the rate of identification on crack characteristics, greatly shorten diagnosis time and realize high-precision diagnosis.

Description

[0001] technical field [0002] The invention relates to a method for identifying cracks in stretched parts, which is applied to the quality inspection and fault diagnosis system of cold stamping dies, or the state identification and quality monitoring in the metal extrusion forming process. Background technique [0003] The working environment of the deep-drawing processing of deep-drawing parts is extremely harsh. During the forming process, the deep-drawing parts not only have to bear high contact pressure and severe friction, but also bear the periodic changes of stress, strain and temperature caused by cyclic loading. Some small cracks are difficult to detect with the naked eye, but batches of waste products will be produced during the production process. Therefore, it is of great engineering significance to identify the crack state of deep-drawing parts. [0004] The two most critical issues in the identification of cracks are: first, to accurately extract the characte...

AI Technical Summary

Problems solved by technology

When performing feature recognition, it is not known in advance which probability density distribution the feature parameters used for feature recognition obey. Usually, it is assumed that the feature parameters obey the normal distribution for feature recognition. The disadvantages brought about by this are: cannot Guarantee the accuracy of judgment results

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