Reframe the Field of Aerospace Engineering Via Machine Learning: Application and Comparison

Abstract

Machine learning (ML) shows its significant efficiency and excellent performance of computing while handling various complex issues in different areas since the term 'Big Data' emerged in the early 1990s. Big data analysis and ML that are effective at resolving various multi-objective as well as limited optimization issues that emerge in spacecraft design and manufacture, are positioned to benefit the aerospace industry. This research paper demonstrates a radical analysis of the use of ML in aerospace engineering. Spacecraft section focuses on anomaly detection, collision avoidance and attitude control. Artificial satellite part is categorized in satellite communication and default diagnosis while thermoacoustic instabilities detection and lunar landing are the main concentration on rockets. The vast application of machine learning in aerospace engineering certainly boosts the growth of the modern aerospace industry as each collected data contributes to a better trained system for attitude control, navigation and default diagnosis which significantly increase the success rate of future space exploration missions.