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) range "∆𝐾" 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 × 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.

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Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization–neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution.