The main focus of the proposed research regards the development of innovative procedures rooted into Augmented Reality (AR) and Virtual Reality (VR) technologies for implementation in the Assembly, Integration and Testing (AIT) process of aerospace systems, leading to the realization of a proof-of-concept. As a byproduct, the buildout of such capabilities is also envisioned to give room for promising collaborations and instrumental contributions in the field of Virtual Education (VE) and Virtual Medicine (VM).
The core of the proposed research is based on Industry 4.0 and Smart Manufacturing concepts. These novel paradigms have led a number of new commercial players to enter the aerospace industry, creating completely new business models and changing the rules of the game. This has posed a potential threat for the long-time industry players whose traditional production models start to be called into question. The adoption of new models in the organization of serial production activities has thus become essential for gaining efficiency and competitiveness. In this frame, the objective of the proposed research is to improve the aerospace systems end-to-end development process, reducing engineering lead time and budget resorting to innovative techniques in the field of AR and VR. This leads to realize a proof-of-concept of a VR/AR-based AIT aerospace process, preventing anomalies and failures and reducing inefficiencies in the concept-to-production path.
The proposed research is organic to ESA's efforts in promoting the design of aerospace systems towards production, integration and verification steps capable to feed relevant lessons learned from the late phases of production back to design. This challenge has proved particularly relevant in the field of satellites mega constellations which sees the current involvement of the components of this research [1].
[1] 'Smart Manufacturing for Future Constellations', ESA ITT AO/1-10002/19/NL/AR for Technology Development.
The application of Industry 4.0 paradigm in the aerospace field brings up the need for technologies and processes to spin-in, finding innovative ways to reduce time and associated effort for production, assembly, integration and testing. The challenge is to design aerospace systems towards production, integration and verification, feeding relevant lessons learned from the late phases back to the design, hence reducing complexity and costs of the AIT by replacing wired sensors with "disposable" wireless autonomous sensors [1, 2] for the monitoring of the system during AIT. However, to face and solve this challenge it's necessary first of all to adopt an adequate high-level approach, based on some fundamental pillars [3].
Currently, the industrial product development and AIT is managed by resorting to:
- Model Based Systems Engineering (MBSE), one the best practices of System Engineering, associated to the product development and intrinsically linked to the process management. MBSE aim is to decompose the systems complexity.
- Lean Manufacturing, allowing to achieve the targets of Quality, Cost and Delivery (the QCD 'iron triangle') and to the enabling technologies of the Smart Manufacturing.
- Smart Manufacturing, the cutting-edge digital technologies supporting the Industry 4.0 fabrication paradigm, making use of advanced information (AR\VR, AI, ML, data analytics) and manufacturing technologies (ALM, automation) with the purpose to optimize production processes and products.
Nowadays those three approaches are still assumed completely uncoupled, against the evidence of the disruptive power of their mutual and full integration [3]. This integration via an AR\VR-based AIT constitutes the main advancement to be achieved for this project intent and defines the approach fundamentals. Just in time, Lean manufacturing and Agile manufacturing models are only three examples of innovations introduced in production schemes over time [3]. Lean methodologies must be employed when it is necessary to directly minimize time and cost inefficiencies in the design, build and deployment of the systems. Lean production methods originated in the automobile industry. These methods can be applied to the production of aerospace systems as well [4].
The traditional approach places emphasis on high cost and time-consuming quality assurance methods that check and recheck, test and retest and verify and reverify the AIT process. Instead, here we propose to employ AR\VR-based technologies to pave the way to the adoption of Six Sigma methods within the AIT process. Six Sigma methodologies are a set of tools and procedures to improve processes including problem solving, process control and process characterization techniques. Lean Six Sigma (LSS) became popular in 2003 as the marriage of Lean manufacturing and Six Sigma as an integrated method able to drive out waste and non-value producers (lean) and for optimizing value-added processes (six sigma) [4].
The ultimate target of such approach is virtually perfect execution. In this sense, the use of AR\VR within AIT allows to gather information to be sent back in real time to the design phase, using in-control processes which will produce robust design of high-quality products, without associated high costs and long cycle times. Such AR\VR-based AIT process must be incorporated as an early part of the system validation plan.
Currently, it's common practice in many industrial environments to disregard this holistic vision of the problem. Often a perception persists, in fact, that the benefits obtained by the application of advanced optimization techniques are not worth the additional effort required to change consolidated schemes and procedures for the inclusion of such advanced methodologies. The proposed activity will be carried out to prove the contrary, adopting what has been recently defined a Digital Engineering Systematic and Systemic Approach, as described in the ESA Design 2 Produce (D2P) concept [5].
[1] Dargie, W. and Poellabauer, C. (2010). 'Fundamentals of wireless sensor networks: theory
and practice'. John Wiley and Sons. pp. 168-183, 191¿192. ISBN 978-0-470-99765-9.
[2] Sohraby, K., Minoli, D., Znati, T. (2007). 'Wireless sensor networks: technology, protocols, and applications'. John Wiley and Sons. pp. 203-209. ISBN 978-0-471-74300-2.
[3] Eugenio Brusa, 'Synopsis of the MBSE, Lean and Smart Manufacturing in the product and process design for an assessment of the strategy Industry 4.0', CIISE 2018, Conferenza INCOSE Italia su Systems Engineering, Rome (Italy), 28-30 November 2018
[4] J. R. Wertz, 'Space Mission Engineering: The New SMAD', Space Technology Library, January 2011
[5] ESA, Design to produce, Cross cutting initiative, 2018, https://www.esa.int/Our_Activities/Space_Engineering_Technology/Design_2...