The very-high-cycle fatigue (VHCF) behavior of material BG2532, used in oil and gas completion strings, was investigated under both non-corrosive and hydrogen sulfide (H2S) gas corrosion conditions. During the experiment, the material’s fatigue property and fatigue fracture characteristics were studied. Additionally, the microstructure on the axial cross-section, perpendicular to the fatigue fracture surface, was analyzed to explore the mechanism of corrosion-induced VHCF crack initiation. To enable unified VHCF life prediction for the material under both corrosive and non-corrosive conditions, different VHCF life prediction models were developed. Fatigue fracture characteristics, including the number of grains per unit area on fatigue source and the facet ratio on propagation area, were proposed as key parameters for VHCF modeling. Two artificial intelligence (AI)-fatigue models incorporating corrosion effects were developed and compared. The results show that integrating fatigue source and propagation characteristics using deep learning and convolutional neural networks significantly enhances the accuracy of VHCF life predictions, with errors remaining within a factor of two. This model effectively predicts the VHCF life of BG2532 alloy under both corrosive and non-corrosive conditions.

中文翻译：

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完井柱中的腐蚀前极高周 AI 疲劳

研究了用于石油和天然气完井柱的材料 BG2532 在非腐蚀性和硫化氢 （H2S） 气体腐蚀条件下的超高周疲劳 （VHCF） 行为。在实验过程中，研究了材料的疲劳性能和疲劳断裂特性。此外，还分析了垂直于疲劳断裂表面的轴向横截面上的微观结构，以探索腐蚀诱导的 VHCF 裂纹萌生的机制。为了能够在腐蚀性和非腐蚀性条件下对材料进行统一的 VHCF 寿命预测，开发了不同的 VHCF 寿命预测模型。疲劳断裂特性，包括疲劳源上每单位面积的晶粒数和扩展面积上的刻面比，被提出作为 VHCF 建模的关键参数。开发并比较了两个包含腐蚀效应的人工智能 （AI） 疲劳模型。结果表明，使用深度学习和卷积神经网络整合疲劳源和传播特性可显著提高 VHCF 寿命预测的准确性，误差保持在 2 倍以内。该模型有效地预测了 BG2532 合金在腐蚀和非腐蚀条件下的 VHCF 寿命。