Crack propagation life prediction of a single lap shear joint: a linear elastic fracture mechanics based machine learning approach - YAYLA, PAŞA (My University, 2022)
Estimation of fatigue life in the aerospace industry plays a key role in terms of safety, certification, and inspection. Individual aircraft tracking programs aim to calculate the fatigue index for each aircraft based on its load history. Therefore, accurate estimation of fatigue life based on unique load spectra for each aircraft plays a crucial role. The purpose of this study is to predict the crack growth (CG) life of a single lap shear joint for an aircraft based on different spectral loads using random forest regression and k-nearest neighbors(k-NN) regression. A finite element model was built using the Huth fastener flexibility method to obtain fastener loads and stress values on the structure under maneuver loads. Then, finite element results were compared to analytical calculations. Based on Fighter Aircraft Loading Standard for Fatigue and Fracture (FALSTAFF) spectra, 90 different spectra were developed to be used for the calculation of CG life. The AFGROW software was used to calculate the CG life based on the load spectra and the findings of the finite element model. Analytical calculations of CG lives and features of individual load spectrums were fed to machine learning models. Finally, the CG life predictions of the machine learning models were compared with analytical calculations. According to the findings, a good correlation was observed between the analytical and predicted CG lives.
Keywords: Fatigue; Crack; Machine Learning; Aircraft