The paper is aimed at developing an efficient and robust probabilistic fatigue life prediction framework for adhesively bonded joints. This framework calibrates the fatigue life model by quantifying uncertainty in the fatigue damage evolution relation using a set of experimental fatigue life data. Probabilistic assessment of fatigue life is simulated through damage evolution along the bondline and Bayesian inference via the Markov chain Monte Carlo (MCMC) sampling method for inverse uncertainty quantification (UQ). To expedite the fatigue life simulation, a hybrid model composed of physics-based fatigue damage evolution relation and a data-driven artificial neural networks (ANNs) model is employed. The degradation of the adhesive is evaluated by the fatigue damage evolution relation which is then mapped to the strain redistribution along the bondline using the ANNs model. Once the mapping is learned by the ANNs, through data from FEA simulations, the probabilistic fatigue life prediction framework involves three successive modules: (I) fatigue damage growth (FDG) simulator, (II) uncertainty quantification (UQ), and (III) confidence bounds for fatigue life prediction. The FDG simulator can be used for simulating fatigue degradation rapidly for a given geometric configuration under any arbitrary fatigue loading spectra. The quantified uncertainties from the framework correspond to the intrinsic statistical material properties that can be used for probabilistic fatigue life prediction in any joint configuration with the same adhesive material. The probabilistic framework is verified using a single lap joint (SLJ) by quantifying uncertainties which are then used for probabilistic fatigue life prediction in laminated doublers in the bending (LDB) joint, that uses the same adhesive material as SLJ, and successfully compared with experimental data. The framework is also tested and validated by estimating probabilistic fatigue life in other joint configurations under constant and variable amplitude fatigue loading spectra.

该论文旨在为粘接接头开发一种高效且稳健的概率疲劳寿命预测框架。该框架通过使用一组实验疲劳寿命数据量化疲劳损伤演化关系中的不确定性来校准疲劳寿命模型。疲劳寿命的概率评估是通过沿粘合线的损伤演化和贝叶斯推理通过马尔可夫链蒙特卡罗 (MCMC)逆采样方法来模拟的不确定性量化 (UQ)。为了加快疲劳寿命模拟，采用了由基于物理的疲劳损伤演化关系和数据驱动的人工神经网络 (ANN) 模型组成的混合模型。粘合剂的退化通过疲劳损伤演化关系进行评估，然后使用人工神经网络模型将其映射到沿粘合线的应变重新分布。一旦神经网络学习到映射，通过 FEA 模拟的数据，概率疲劳寿命预测框架涉及三个连续的模块：(I) 疲劳损伤增长 (FDG) 模拟器，(II) 不确定性量化 (UQ)，和 (III)疲劳寿命预测的置信界限。FDG 模拟器可用于在任意疲劳载荷谱下快速模拟给定几何配置的疲劳退化。来自框架的量化不确定性对应于固有的统计材料特性，可用于在具有相同粘合剂材料的任何接头配置中进行概率疲劳寿命预测。概率框架是使用单搭接接头 (SLJ) 通过量化不确定性来验证的，然后将不确定性用于弯曲 (LDB) 接头中的层压板中的概率疲劳寿命预测，该接头使用与 SLJ 相同的粘合剂材料，并成功地与实验进行比较数据。该框架还通过估计恒定和可变振幅疲劳载荷谱下其他关节配置的概率疲劳寿命来测试和验证。