EXPLORING DECISION-MAKING IN AERONAUTICAL ENGINEERING THROUGH ARTIFICIAL INTELLIGENCE TECHNIQUE: A SYSTEMATIC REVIEW OF THE LITERATURE

   The aeronautical industry has faced, over the last decade, increasing challenges in relation to decision-making in the face of increasingly complex systems, requiring efficient, safe and data-based solutions. With the consolidation of Industry 4.0, there has been a significant expansion in the adoption of artificial intelligence (AI) techniques applied to aeronautical engineering. Therefore, this study had as its main objective to investigate how AI techniques were applied in decision-making in this sector. The analysis included articles published between 2014 and 2024, extracted from the IEEE Xplore, Scopus, and Web of Science databases, resulting in a sample of 42 studies.

     The results indicated a consistent growth in the number of publications on the subject from 2018 onwards, revealing an increase in academic and technical interest in AI-based solutions. The most recurrent techniques included supervised machine learning, artificial neural networks, fuzzy logic , and genetic algorithms. These approaches were mostly applied in four main contexts: predictive maintenance, air traffic management, decision support in flight-critical situations, and aircraft design optimization.

  The analysis showed that AI was used more frequently to anticipate failures in aeronautical systems, optimize routes, and increase the reliability of operational decisions