Abstract
Aircraft manufacturing industries often evolve in the ecosystem of complex designs and manufacturing processes associated with large volume of information generated along the lifecycle. Digital Twin (DT) technology has the potential of leveraging such information to provide useful insights benefiting the overall business in many ways. Information Management (IM) for DT is still an ongoing challenge for many industries, thus leaving a considerable research gap. In this paper, an IM framework for DT in the aircraft manufacturing sector is proposed. The key phases and elements of IM are discussed on which the framework is constructed. The potential application of the framework along aircraft lifecycle is further discussed. The framework not only provides an effective approach to managing information but also opens new research prospects in DT domain.
Highlights
AAibrsctrraaftctmanufacturing industries often evolve in the ecosystem of complex designs and manufacturing processes associated with large volume
As Digital Twin (DT) is heavily driven by information across the asset or system, Information Management (IM) becomes one of the key aspects to support multiple services-based business models, maintainability of information and long-term system sustainment
DT has the ability of real-time control and optimisation of product and production lines in manufacturing environments [11], but the cost of developing and maintaining DT must be driven by both business and economic models of the industry
Summary
Aircraft and its systems are designed, developed and maintained for the long-term sustainment. The average airline lifespan for a single-aisle aircraft is around 2530 years with adequate periodic maintenance checks [3] Maintaining such systems for long-term sustainment needs dedicated IM infrastructure and tools. Agniva [6] proposed a way of formalising knowledge as DT models coming from sensors of industrial production lines. This approach uses a Graph-based Query Language (GQL) equivalent to conjunctive queries and has been enriched by inference rules. Looking at the DT data management side, Zhang [7] proposed an approach to design and develop DT of production line based on semantic data model as a reference model and synchronisation of equipment at the physical level.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
