Prediction of Fatigue Crack Growth Rate in Aircraft Aluminum Alloys Using Optimized Neural Networks [post]

Hassan Bin Younis, Khurram Kamal, Muhammad Fahad Sheikh, Amir Hamza
2020 unpublished
In aerospace industry, Fatigue Crack Propagation pose a serious threat in designing mechanical assembly of the aircraft structures. In these structures crack growth is a problem to be handled seriously, as human life risk is concerned in addition to economic loss. Fatigue Crack Growth (FCG) Rate is the rate at which crack grows with number of cycles subjected to constant amplitude loading. Upon analyzing the curve it becomes obvious that the correlation between Stress Intensity Factor (SIF)
more » ... ty Factor (SIF) range "DK " with FCG rate "da/dN " is deviating linear relationship considering region II of the curve that is also called Paris Region. Empirical formulation methods cannot deal with linearity factor satisfactorily. In contrast to the prior methods, machine learning algorithms are capable to deal with the non-linearity issue in a much better way owing to their admirable learning ability and flexible nature. In this research work Genetic Algorithm, Hill Climbing Algorithm and Simulated Annealing Algorithm based Optimized Neural Networks were utilized for prediction of FCG rate. Proposed technique was validated by testing on different aerospace aluminum alloys including 2324-T39, 7055-T7511 and 6013-T651. The least predicted MSE was 1.0559 x 10-9 achieved for aluminum alloy 6013-T651 by Simulated Annealing based optimized Neural Network. Moreover, the results demonstrate an exceptional conformity to the data conceived during experimentation process.
doi:10.21203/rs.3.rs-113296/v1 fatcat:oeefrgklzvgh3lxlp6ye6thvga