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A Fatigue Crack Growth Rate Prediction Method Based on Artificial Neural Network

A technology of fatigue crack propagation and neuron network, which is applied in the application field of artificial neuron network, can solve problems such as not being completely linear, and achieve the effect of comprehensive methods, strong scalability and strong expansibility

Inactive Publication Date: 2018-11-06
BEIHANG UNIV
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Problems solved by technology

However, the actual fatigue fracture curve is not completely linear even in the logarithmic coordinates of zone II

Method used

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  • A Fatigue Crack Growth Rate Prediction Method Based on Artificial Neural Network
  • A Fatigue Crack Growth Rate Prediction Method Based on Artificial Neural Network
  • A Fatigue Crack Growth Rate Prediction Method Based on Artificial Neural Network

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specific Embodiment

[0056] The present invention uses experimental data to train the artificial neuron network as the following steps:

[0057] Step 1: Obtain the load Kmax, stress ratio R, and fatigue crack growth rate da under the corresponding load; import the experimental data, and refer to the specific data figure 1 : Including stress intensity factor series Kmax, stress ratio series R, single crack growth length series da;

[0058] Step 2: Preprocess the experimental data: first logarithmize the experimental data Kmax, da, and then normalize the experimental data, and obtain the relevant normalization parameters ps1, ps2 at the same time;

[0059] Step 3: Adjust the mean square error target of the artificial neuron network, the expansion speed of the radial basis function, the maximum number of neurons, and use the normalized data to train the artificial neuron network;

[0060] Step 4: Create arrays tx1, tx2, ty, assign 1000 numbers uniformly distributed between 0 and 1 to tx1, assign 100...

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Abstract

The invention discloses a fatigue crack growth rate prediction method based on an artificial neuron network. The fatigue crack growth rate prediction method utilizes the outstanding fitting capability of the artificial neuron network on a multielement non-linear mapping relationship to carry out exploitative learning in experiment data and finally describe the mapping relationship between fatigue load (stress intensity factor and stress ratio) and a corresponding crack growth rate, and a fatigue crack growth rate prediction algorithm is established on the basis of the mapping relationship. The fatigue crack growth is a highly non-linear process, and an influence on the fatigue crack growth by a stress ratio is non-linear, which is shown in the specification. The traditional classical theory is characterized in that the fatigue crack growth rate is taken as a linear process under a log-log coordinate to carry out calculation. Since the artificial neuron network is used for predicting the fatigue crack growth rate, the characteristic of high non linearity of the fatigue crack growth can be fit, and meanwhile, the influence on the fatigue crack growth rate by the stress ratio can be favorably described.

Description

technical field [0001] The present invention relates to the application field of artificial neuron network, and more specifically relates to a fatigue crack growth rate prediction method based on artificial neuron network. Background technique [0002] In today's aviation industry, the theory of damage tolerance is widely recognized and applied to the structural design of aircraft. Therefore, the theory and method of fatigue crack growth based on linear elastic fracture mechanics (LEFM) are widely used in the prediction of fatigue life of material structures. In the 1960s, Paris was the first to link the crack growth rate and the magnitude of the stress intensity factor and successfully applied the LEFM theory to the fatigue crack growth problem of metallic materials. [0003] Since the 1980s, due to the good simulation of the structure and function of the human brain by the artificial neural network, it has been extensively studied and applied to various industries. Up to...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/18G06N3/02
CPCG06N3/02G16Z99/00
Inventor 包章珉张慰姜珊王强
Owner BEIHANG UNIV
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