Welding structure fatigue performance analysis method based on data driving method

Abstract

The invention discloses a welding structure fatigue performance analysis method based on a data driving method, and relates to the field of welding structure fatigue performance analys.The welding structure fatigue performance analysis method comprises the following steps that fatigue performance data are obtained, a multi-scale fatigue performance database is established, and the data in the database is divided into training data and test data; analyzing a linear correlation degree and a non-linear correlation degree between the fatigue performance and the influence factors by using a Pearson correlation coefficient and a maximum information coefficient respectively; based on an optimized gradient lifting algorithm, carrying out quantitative analysis on the weights of the fatigue performance influence factors; training a deep convolutional neural network by using the training data; inputting the fatigue performance influence factors into the trained convolutional neural network to obtain a prediction parameter curve; and extracting the fatigue life according to the predicted parameter curve. According to the invention, accurate and stable fatigue life prediction of the complex welding structural member under different materials, shapes, sizes, processing technologies and service conditions can be realized.

Description

technical field [0001] The invention relates to the field of fatigue performance analysis of welded structures, in particular to a method for analyzing fatigue performance of welded structures based on a data-driven method. Background technique [0002] For a long time, the fatigue performance of welded structures has been difficult to systematically analyze due to the large consumption of manpower and material resources in fatigue testing, especially the fatigue behavior of welded structural parts is affected by many factors and large discreteness. The research on fatigue performance can usually be divided into five methods: test method, analytical solution method, numerical simulation method, artificial intelligence technology and the combination of current expert experience and data-driven method. The results of today's fatigue life prediction based on artificial intelligence technology show that the hybrid intelligent algorithm is an effective means to accurately predict...

AI Technical Summary

Problems solved by technology

However, most of the current fatigue life predictions are based on machine learning and deep learning to directly predict the fatigue life under a certain stress level. However, due to the discrete nature of fatigue performance, it is easy to lead to prediction results with particularly large errors, so it is difficult to describe stably.

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