Industrial Applications of AI in Aircraft Manufacturing: A PRISMA Systematic Literature Review

Industrial Applications of AI in Aircraft Manufacturing: A PRISMA Systematic Literature Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Industrial Applications of AI in Aircraft Manufacturing: A PRISMA Systematic Literature Review Pierrick BOUGAULT, Raphael Anwar EL HADDAD, Liang MA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6199323/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Production Engineering Archives → Version 2 posted You are reading this latest preprint version Show more versions Abstract Artificial Intelligence (AI) and Machine Learning (ML) are foundations in new manufacturing paradigms, yet their application in the aircraft industry remains limited, as this industry's core expertise does not traditionally cover these technologies. Additionally, due to its specific features, the aircraft industry presents unique challenges, for instance with data. To date, no systematic review has considered these features to enable stakeholders in this sector to successfully undergo AI/ML transformation. This study aims to analyze and screen the state of the art by providing a PRISMA systematic literature review of 89 articles, focusing on the contexts, models, and methods employed in the development of AI/ML solutions. The authors propose a framework to summarize the findings regarding the AI development, applications, benefits, and challenges of AI/ML in the aircraft manufacturing industry. This study contributes to the field by meticulously gathering methodologies and approaches that address and integrate the specificities of AI/ML use and integration in this industry. Furthermore, further research opportunities are identified through a comparison of current research applications, theoretical concepts of Industry 5.0, and cutting-edge technologies, such as Federated Learning, Transfer Learning, the use of Large Language Models (LLMs), the lack of supply chain investigation, and the integration of human factors, which are emerging or notably absent in major reviewed articles. Industrial Engineering Artificial Intelligence and Machine Learning Machine learning Artificial Intelligence Aircraft Industry Aircraft Manufacturing Prisma Literature Review Industry 5.0 Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Production Engineering Archives → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6199323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":427680282,"identity":"063f4f2b-c01e-4923-a0a5-f45b070cc67c","order_by":0,"name":"Pierrick 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