A Novel Method for Aging Life Evaluation of O-Rings Based on the Sealing Performance Degradation Model and the Artificial Neural Network Model

Abstract

The effectiveness and stability of sealing structures and O-rings in engineering not only impact the performance and safety of engineering systems but also directly affect the operational lifespan and maintenance costs of equipment. This study proposes an aging life evaluation method for sealing rings based on a sealing performance degradation model and an artificial neural network (ANN) model. First, the impact of compression ratios on the sealing performance of the O-ring was analyzed using comprehensive macro–micro numerical simulations. Next, accelerated aging tests were conducted under five different temperature conditions, and a performance degradation model for the O-ring was developed based on a dynamic curve model, expanding the degradation data. Furthermore, an aging life evaluation method for O-rings based on the ANN model is proposed to predict the aging life of O-rings under different temperatures and compression sets. The results indicate that in practical applications, an appropriate compression ratio for the O-ring should be selected, and the sealing structures with smaller Von Mises stress should be prioritized under the condition that the contact stress is greater than the medium pressure. Moreover, the established O-ring performance degradation model aligns well with the experimental results. The proposed ANN model demonstrates good effectiveness in predicting the aging life of O-rings under different operating conditions and selected sets. The ANN model achieved a root mean square error (RMSE) of 1.8264, a coefficient of determination (R2) of 0.9999, and a mean absolute percentage error (MAPE) of 8.05%, demonstrating high prediction accuracy and generalization capability. This method offers an effective approach for engineering sealing structure design and aging life prediction of sealing rings.