Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys

Abstract

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.

Quantitative Detection for Fatigue Natural Crack in Aero-Aluminum Alloy Based on Pulsed Eddy Current Technique

“…Due to the continuous changing loads on aircraft during work, fatigue cracks will occur [1,2], which is an important cause of safety accidents. Consequently, there is significant attentions paid to the initiation and propagation of cracks, leading to research efforts aimed at understanding the formation and expansion of cracks in engineering materials like aeroaluminum alloys [3][4][5][6]. On the other hand, some of the attentions are involved in seeking the effective detection of cracks to ensure the safety operation of equipment [7][8][9].…”

Automatic Explanation Of Metal’s Phenomena By Application Of Artificial Neural Networks – Use Case For Hot Extruded Aluminum Alloy 6082

“…Additionally, research investigates predicting tool wear during milling of aluminum matrix composites using artificial neural networks [9], as well as predicting the flow behavior of aluminum alloys during hot compression tests [10]. Other studies explore using deep neural networks for predicting fatigue crack growth in aluminum aircraft alloys [11] and analyzing the inclusion of ceramic particles in aluminum matrix composites through stir casting [12]. Furthermore, research focuses on predicting mechanical properties of aluminum alloys [13], analyzing frictional performance of aluminum alloy sheets [14], and optimizing machining parameters for aluminum alloys using machine learning algorithms [15].…”