Research on Time-Domain Fatigue Analysis Method for Automotive Components Considering Performance Degradation

Automotive components’ exposure to prolonged random loading not only accumulates fatigue damage but also causes material stiffness degradation. The degradation of material mechanical properties leads to stress redistribution within the structure, which in turn affects the structural fatigue life. Conventional frequency-domain fatigue life analysis methods often fail to take into account performance degradation, whereas time-domain approaches are constrained by computational inefficiency in dynamic response calculations. To address this, a time-domain fatigue life analysis is proposed, integrating Long Short-Term Memory (LSTM) networks with performance degradation modeling. First, short-term dynamic response data of engineering structures that contain stiffness degradation parameters are utilized to establish a training set, and an LSTM surrogate model is trained to rapidly predict stress responses in time- and degree-varying structural performance degradation. Second, the time-varying dynamic responses obtained from the LSTM surrogate model are related to the principles the fatigue damage accumulation and Miner’s criterion to quantify the stiffness degradation effects. A computational framework has been developed for fatigue life prediction through iterative alternation between dynamic response calculations and fatigue damage assessments. Case studies on notched plates demonstrate that the LSTM surrogate model approach ensures accuracy while reducing structural fatigue life analysis time by more than three orders of magnitude compared to the finite element method (FEM). Under the application of 20,000s random road loads, the damage value of the reinforced plate obtained by the surrogate model method that takes into account performance degradation is lower by 10–25% compared to that calculated by the frequency-domain or time-domain methods that neglect degradation.