人工神经网络中的耦合物理学预测腐蚀钢丝的疲劳行为

IF 5.7 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue Pub Date : 2024-10-24 DOI:10.1016/j.ijfatigue.2024.108669

Fan Yi , Huan Lei , Qingfang Lv , Yu Zhang

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引用次数: 0

摘要

为了准确预测腐蚀钢丝的疲劳寿命，作者提出了一个人工神经网络（ANN）模型和一个概率物理引导神经网络（PPgNN）模型。应力范围、平均应力和腐蚀率等因素被视为这两个神经网络的输入特征。ANN 模型的预测精度最高，其确定系数为 0.94；但它无法捕捉疲劳寿命的分散性。通过引入物理约束，PPgNN 模型能够预测疲劳寿命的标准偏差。结果表明，94.79% 的数据集在 95% 的置信区间内。

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Coupling physics in artificial neural network to predict the fatigue behavior of corroded steel wire

To accurately predict the fatigue life of corroded steel wire, the authors proposed an artificial neural network (ANN) model and a probabilistic physics-guided neural network (PPgNN) model. Factors including stress range, mean stress, and corrosion rate were considered as input features of these two neural networks. The ANN model exhibited the best prediction accuracy with a determination coefficient of 0.94; however, it cannot capture the dispersion of fatigue life. Through introduction of physics constraints, PPgNN model was able to predict the standard deviation of fatigue life. The results showed that 94.79% of the dataset was within the 95% confidence interval.

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来源期刊

International Journal of Fatigue 工程技术-材料科学：综合

CiteScore

10.70

自引率

21.70%

发文量

619

审稿时长

58 days

期刊介绍： Typical subjects discussed in International Journal of Fatigue address: Novel fatigue testing and characterization methods (new kinds of fatigue tests, critical evaluation of existing methods, in situ measurement of fatigue degradation, non-contact field measurements) Multiaxial fatigue and complex loading effects of materials and structures, exploring state-of-the-art concepts in degradation under cyclic loading Fatigue in the very high cycle regime, including failure mode transitions from surface to subsurface, effects of surface treatment, processing, and loading conditions Modeling (including degradation processes and related driving forces, multiscale/multi-resolution methods, computational hierarchical and concurrent methods for coupled component and material responses, novel methods for notch root analysis, fracture mechanics, damage mechanics, crack growth kinetics, life prediction and durability, and prediction of stochastic fatigue behavior reflecting microstructure and service conditions) Models for early stages of fatigue crack formation and growth that explicitly consider microstructure and relevant materials science aspects Understanding the influence or manufacturing and processing route on fatigue degradation, and embedding this understanding in more predictive schemes for mitigation and design against fatigue Prognosis and damage state awareness (including sensors, monitoring, methodology, interactive control, accelerated methods, data interpretation) Applications of technologies associated with fatigue and their implications for structural integrity and reliability. This includes issues related to design, operation and maintenance, i.e., life cycle engineering Smart materials and structures that can sense and mitigate fatigue degradation Fatigue of devices and structures at small scales, including effects of process route and surfaces/interfaces.