Ferromagnetic material hardness prediction algorithm based on improved BP neural network algorithm

Abstract

Ferromagnetic material hardness prediction algorithm based on improved BP neural network algorithm. Firstly, the Barkhausen signal of ferromagnetic material is collected, the signal set is divided, and the Barkhausen noise training set and Barkhausen noise test set are obtained. Then the collected signal is analyzed by AR spectrum. The second derivative of the expanded signal is obtained by choosing five orders of expansion, which are 4, 8, 16, 32 and 64 respectively. The valley width, the valley depth and the position of the valley point of the second order derivative signal are taken as thecharacteristics. The valleys are encoded by kmeans algorithm, thus the unification of the characteristic dimensions is achieved Then the BP neural network model is optimized and trained. The simulation results show that the prediction result of the invention is very good, and the mean square error is only 80, that is, the error of each hardness prediction can be guaranteed to be 9 Vickers hardness, and the mean square error of the time domain algorithm is 229, that is, more than 15 Vickers hardness, so the algorithm is proved to be effective.

Description

technical field [0001] The invention relates to a non-destructive detection algorithm for ferromagnetic materials. According to Barkhausen noise, the traditional time-domain characteristics and BP neural network are improved, and a ferromagnetic material hardness prediction algorithm with higher accuracy is designed, which belongs to regression analysis and non-destructive detection. Related field. Background technique [0002] In the machinery, automobile, aerospace, petrochemical, national defense, military and power industries, the monitoring and prediction of component fatigue life is crucial. The microstructure of the material is one of the important factors affecting its operating life . Therefore, rationally controlling the production, processing and use of materials and reducing structural defects are important measures to ensure and prolong the service life. The hardness of ferromagnetic materials depends on its structure, that is, the change of the internal micro...

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Problems solved by technology

[0006] Third, it has a full process. The destructive test can only test the raw materials. After the finished product is made, whether it is before leaving the factory or in use, unless they are not prepared to continue to serve, destructive tests cannot be carried out, and the steel cables of the bridge , rails and other facilities, people just want to test whether they can continue to serve. After the damage detection is destroyed, they will be scrapped regardless of whether they can continue to serve. Therefore, non-destructive testing that does not destroy the performance of the test object is the only method that can be used.

[0008] The shortcomings of the existing methods: on the one hand, the traditional time-domain characteristics will be affected by other properties of the metal (such as temperature, residual stress, plastic deformation, etc.), so that the time-domain characteristics cannot form a single variable problem with the hardness, and finally On the other hand, because the initial weight of the BP neural network will affect the final training result of the neural network, and the initial weight of the BP neural network is randomly set, so the final training result of the BP neural network is not Stablize

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