Accurate prediction of fatigue life and structural integrity design of high-performance composite materials for extreme service environments

Abstract

In aircraft and other deep-sea extreme service environments, factors such as the multiaxial loads, temperature gradient, and corrosive medium present complex fatigue damage mechanisms, causing considerable fatigue life prediction errors in the application of high-performance composite materials. Existing empirical formulas fail to adequately describe the relationship between microscopic damage evolution and macroscopic performance degradation. In this article, a multi-scale damage constitutive model and a machine learning algorithm are merged to build a fatigue life prediction system. A micromechanical model of debonding and propagation of microcracks around the fiber-matrix interface is developed, and the Continuum Damage Mechanics (CDM) theory is used to express the accumulation of damage. A hybrid prediction model combining finite element simulation and Convolutional Neural Network (CNN) is constructed, and the acoustic emission signal feature extraction is performed, which realizes the real-time identification of damage states. The accelerated testing protocol is intended for use in high temperature oxidation, salt spray corrosion, and provides S-N curve data at various stress ratios. In this article, a residual strength prediction model is proposed based on the corrected Paris propagation law and a random forest algorithm is used to optimize the inversion of material parameters. Experimental verification shows that the deviation between predicted life and measured value is controlled within ±5.5%, and the average error of critical crack length of 10.2% meets the 12.7% threshold requirement, supporting the damage-tolerant design of key components such as pressure hulls and blades. This method provides a theoretical basis and an engineering practice path for structural integrity assessment under complex service conditions.