A Review of Machine Learning Applications in Aviation Engineering

A Review of Machine Learning Applications in Aviation Engineering

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This review paper investigates how machine learning (ML) has transformed multiple facets of aviation engineering.The work demonstrates substantial progress in flight operations and air traffic management (ATM) optimization through frameworks such as Reinforcement-Learning-Informed Prescriptive Analytics (RLIPA) and deep reinforcement learning (DRL) techniques applied to conflict resolution.The study highlights how ML contributes to operational efficiency through faster computational processes and better decision-making abilities for those who control air teal horse blanket traffic.The paper examines how leading firms such as SpaceX and rubbermaid 8 gallon trash can Raytheon use ML technology to enhance manufacturing processes, including predictive maintenance (PdM) and autonomous systems development.

The paper discusses ML implementation obstacles, including model interpretability, and highlights further research requirements for adapting to real-world issues such as changing traffic volumes and weather variations.Overall, the study demonstrates how ML technology can transform aviation engineering through enhancements in safety standards as well as operational and process efficiency.