A review of machine learning applications in aviation engineering

Koul, Parankush

doi:10.7862/rm.2025.2

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Tytuł artykułu

A review of machine learning applications in aviation engineering

Autorzy

Koul Parankush

Treść / Zawartość

Pełne teksty:

1854-Article Text-5102-1-10-20250207.pdf

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Identyfikatory

DOI

10.7862/rm.2025.2

Warianty tytułu

Przegląd zastosowań uczenia maszynowego w inżynierii lotniczej

Języki publikacji

Abstrakty

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 traffic. The paper examines how leading firms such as SpaceX and 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.

W niniejszym artykule przeglądowym przedstawiono w jaki sposób uczenie maszynowe (ang. machine learning - ML) przekształciło wiele aspektów inżynierii lotniczej. Artykuł przedstawia znaczny postęp w optymalizacji operacji lotniczych i zarządzania ruchem lotniczym poprzez preskryptywną analizę opartą na uczeniu się przez wzmacnianie i techniki głębokiego uczenia się ze wzmocnieniem stosowane do rozwiązywania konfliktów. Badanie podkreśla w jaki sposób ML decyduje o wydajności operacyjnej poprzez szybsze procesy obliczeniowe i lepsze zdolności podejmowania decyzji przez osoby kontrolujące ruch lotniczy. Artykuł analizuje, w jaki sposób wiodące firmy, takie jak SpaceX i Raytheon, wykorzystują technologię ML do ulepszania procesów produkcyjnych, w tym utrzymania predykcyjnego i rozwoju systemów autonomicznych. Omówiono również przeszkody we wdrażaniu ML, w tym interpretowalność modelu, oraz wskazano dalsze wymagania badawcze dotyczące dostosowywania się ML do rzeczywistych problemów, takich jak zmieniające się natężenie ruchu i wahania pogody. Ogólnie rzecz biorąc, wyniki badań omówione w artykule przedstawiają w jaki sposób technologia ML może wspomagać inżynierię lotniczą poprzez udoskonalenie norm bezpieczeństwa, a także wydajność operacyjną i procesową.

Słowa kluczowe

machine learning aviation engineering predictive maintenance air traffic management integration

uczenie maszynowe inżynieria lotnicza utrzymanie predykcyjne zarządzanie ruchem lotniczym integracja

Wydawca

Oficyna Wydawnicza Politechniki Rzeszowskiej

Czasopismo

Advances in Mechanical and Materials Engineering

Rocznik

2025

Tom

Vol. 42, nr 1

Strony

16--40

Opis fizyczny

Bibliogr. 100 poz., tab., wykr.

Twórcy

autor

Koul Parankush

pkoul2.iit@gmail.com

Department of Mechanical and Aerospace Engineering, Illinois Institute of Technology, United States of America

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).

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Bibliografia

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