Akademik Veri Yönetim Sistemi
Defence Science Journal, cilt.74, sa.1, ss.119-126, 2024 (SCI-Expanded)
Estimating fatigue life is challenging due to the input parameters’ statistical natures, such as the manufacturing process, scatter of service loads, microstructure, etc. Regarding fatigue life calculation in the aerospace industry, the importance of an accurate estimation becomes more critical due to strict safety, certification, service costs, and competitiveness regulations. The ability of soft computing methods to reveal complex relationships between multiple parameters and their computational speed could help predict fatigue life, especially in the service. This study compares random forest regression and artificial neural network methods to estimate the crack growth life of a fighter jet aircraft wing joint in terms of their computational time and accuracy. In addition, permutation feature importance and hyper parameter optimisation studies are conducted to extract essential features, investigate their effects on estimation performance, and fine-tune model parameters. The analysed joint is made of 7050-T7451 aluminium, widely used as a structural element in the aerospace industry. Since a hole is one of the major sources of stress concentration, and there may be many holes involved in any engineering structure, it is reasonable to assume that fatigue cracks may initiate at some of these holes during the service life of engineering structures. The crack type considered is a thru-crack around a hole, which is more severe than a corner crack. Load spectra are derived using the Fighter Aircraft Loading Standard for Fatigue (FALSTAFF) to calculate crack growth life. Considering particular service load conditions, ninety different spectra are developed, and the crack growth life of the joint is calculated based on linear elastic fracture mechanics correspondingly. Also, to simulate the worst-case scenario, friction between members and the retardation effect of load spectra are not considered when calculating crack growth life. Python’s Tensor Flow and Scikit-learn libraries are utilised to build machine learning models. Then, ninety different load spectra are input for the thru-crack configuration to predict the crack propagation life. Eventually, the crack propagation life predictions of random forest regression and artificial neural network models are compared. The findings indicate that permutation feature importance and hyperparameter-optimisation significantly affect the model’s accuracy and processing time performance.