Guided wave and fuzzy algorithm-based functional gradient structural material characteristic inversion method

Abstract

Provided is a guided wave and fuzzy algorithm-based functional gradient structural material characteristic inversion method. The guided wave group velocity is obtained through forward calculation by applying a Legendre polynomial method, and a volume distribution function of FGM structural material parameters is subjected to inversion identification by means of a fuzzy algorithm. The method comprises the specific steps that 1, a given FGM structure volume distribution function is selected, and the guided wave group velocity in the FGM structure is obtained through a Legendre polynomial method; 2, a fuzzy controller is designed through a genetic algorithm; 3, a fuzzy controller parameter optimization program based on the genetic algorithm is written; 4, a fuzzy controller program is written; 5, multiple guided wave group velocities at the FGM structure low order mode low frequency are input into the designed fuzzy controller, and the FGM material characteristics are output through inversion. Accordingly, an error between a parameter value in the FGM structure volume distribution function obtained through inversion and a set value is very small, and an effective method is provided for FGM material characteristic detection.

Description

technical field [0001] The invention belongs to the technical field of material property inversion, and in particular relates to a method for inversion of material properties of functionally graded structures based on guided waves and fuzzy algorithms. Background technique [0002] Functionally Gradient Materials (FGM for short) is a new type of heterogeneous composite whose composition or structure exhibits regular spatial changes, reducing or even disappearing its internal interface, so that the material properties also exhibit spatial changes. Material. The smooth change of the volume content of FGM components brings various advantages such as reducing residual stress, alleviating stress concentration and enhancing bond strength. It has broad application prospects, and mastering the material properties of FGM is the premise of using it effectively. [0003] Ultrasonic guided waves have dispersion and multi-modal characteristics, that is, at different frequencies, multip...

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Problems solved by technology

In addition, in order to simulate the actual situation, the BP neural network tends to fall into the local maximum value when inverting the noise signal, which makes the error large

At the same time, when the adaptive neuro-fuzzy inference system used in the above literature inverts material properties, there is a Takagi-Sugeno type fuzzy system that only supports a single output, and the number of control inputs cannot be greater than 6 other questions

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