A data driven model for estimating the fatigue life of 7075-T651 aluminum alloy based on the updated BP model

Abstract

pitting corrosion is an important factor, which leads to stress concentration. A sharp stress concentration will have a big impact on the fatigue life of 7075- T651 aluminum alloy. Most of the studies have been conducted to investigate its fatigue performance and proposed some estimated models. However, these models only considered the influence of the depth, the length, and the width of pits on the fatigue life and neglected their coupling effects. In order to predict the fatigue life of 7075- T651 aluminum alloy accurately. First, some numerical models were established to explore the stress concentration factor (SCF) affected by pit sizes. Second, some data driven models were proposed to investigate it. The SCF was regarded as the output and the length, the width, and the depth of pits were acted as the inputs. Third, the fatigue performance of the aluminum alloy 7075-T651 was explored, based on the fracture mechanics theory. The results indicate that the neural network optimized by the sparrow search algorithm not only has a relatively small error but also has stronger robustness, which can predict the SCF well. In addition, the SCF increases, as the length and the width of the pits increase, and decreases, as the pit depth increases. The pit depth can be treated as an important factor to influence the fatigue life of 7075- T651 aluminum alloy.

Keywords

• Pitting corrosion
• Fatigue life
• Aluminum alloy 7075-T651
• Numerical model
• Stress concentration factor
• Machine learning approach