Fretting fatigue of dovetail joints is of paramount importance for ensuring equipment safety, where the swift and precise estimation of their fatigue life is crucial. In this study, we present a nonlocal multiaxial fatigue model based on artificial neural networks (ANN) to tackle these challenges. Initially, the damage parameters were calculated using critical plane approaches (CPA) and theory of critical distance (TCD) analysis, and the fretting fatigue stress was computed. Subsequently, these parameters were integrated as input features in the ANN model to predict the fretting fatigue life of dovetail joints. The predicted results demonstrate that this proposed model can accurately predict the fretting fatigue life of dovetail samples within a 1.5× limit band. Furthermore, a comparative analysis with other ANN models inspired by previous researchers also supports this viewpoint. This capability stems from its integration of ANN representation capabilities with physics and domain knowledge, such as CPA/TCD methods and fretting fatigue stress analysis. This approach not only establishes a theoretical foundation for predicting the fretting fatigue life of dovetail samples but also showcases promising practical applications.

中文翻译：

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基于人工神经网络的非局部多轴疲劳模型预测燕尾接头微动疲劳寿命


燕尾接头的微动疲劳对于确保设备安全至关重要，其中快速、准确地估计其疲劳寿命至关重要。在本研究中，我们提出了一种基于人工神经网络（ANN）的非局部多轴疲劳模型来应对这些挑战。最初，使用临界平面方法（CPA）和临界距离理论（TCD）分析计算损伤参数，并计算微动疲劳应力。随后，将这些参数作为输入特征集成到 ANN 模型中，以预测燕尾接头的微动疲劳寿命。预测结果表明，该模型可以在1.5×极限范围内准确预测燕尾榫样品的微动疲劳寿命。此外，受先前研究人员启发，与其他 ANN 模型的比较分析也支持了这一观点。这种能力源于 ANN 表示能力与物理和领域知识的集成，例如 CPA/TCD 方法和微动疲劳应力分析。该方法不仅为预测燕尾样品的微动疲劳寿命奠定了理论基础，而且展示了有前景的实际应用。