一种基于参数影响的数据驱动下的疲劳寿命预测方法

doi:10.3901/JME.2023.04.071

摘要/Abstract

摘要： 金属构件的主要失效方式是在循环载荷作用下的疲劳破坏，因此金属构件的疲劳寿命预测对于保证结构安全性和可靠性十分必要。能量法是一种既能用于低周疲劳寿命预测，也能用于高周疲劳寿命预测的方法，其以寻找有效的显式能量损伤参量为手段，结合适当的损伤积累方式进行寿命评估。针对材料疲劳寿命预测问题，提出一个基于能量法和人工神经网络算法的疲劳寿命预测方法。为了达到反映不同加载路径影响的目的，从转动惯量的角度引入两个路径相关参量。使用基于应变控制的九种材料的疲劳试验数据对提出的神经网络模型进行训练、测试。结果显示模型对训练数据和测试数据均有良好的预测精度，并可对单轴加载、多轴加载、高周疲劳和低周疲劳寿命进行有效预测，表明本模型在多轴疲劳寿命预测方面具有较广泛的适用性。

关键词: 疲劳, 转动惯量, 神经网络, 寿命预测

Abstract: Fatigue is the main failure mode of metal components under cyclic loading, thus the fatigue life prediction of metal components is very necessary to ensure the safety and reliability of structures. As a method suitable for low- and high-cycle fatigue life prediction, energy-based method is to create an effective explicit energy damage parameter combining with appropriate damage accumulation methods. Aiming at the fatigue life prediction of materials, a fatigue life prediction method based on energy method and neural network algorithm is proposed. In order to reflect the influence of different loading paths, two loading-path-dependent parameters are introduced based on the moment of inertia concept. Strain-controlled fatigue experimental results of nine materials are used to train and test the proposed neural network model. It can be found that the model not only has good prediction accuracy for both the training and test data, but also can effectively predict uniaxial loading, multi-axial loading, high cycle fatigue and low cycle fatigue life, which indicates the model has a wide range of applicability in multiaxial fatigue life prediction.

Key words: fatigue, moment of inertia, neural network, life prediction

中图分类号:

O346

刘志状, 吴昊. 一种基于参数影响的数据驱动下的疲劳寿命预测方法[J]. 机械工程学报, 2023, 59(4): 71-79.

LIU Zhizhuang, WU Hao. Data-driven Fatigue Life Prediction Method Based on the Influence of Parameters[J]. Journal of Mechanical Engineering, 2023, 59(4): 71-79.

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